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02-22-2025, 03:32 PM
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Help using AV to create FOF9 player files
Apologies for going On Topic in the Off Topic area, but I thought this would get a bigger audience here so I could get more input.
I'm looking for ideas in using the "Approximate Value" stat from Pro Football Reference, and getting good ratings for players in FOF9. I'm going to start with QBs, since they have their own file, and since there's more opinions on which QBs are good. For those unaware of how FOF9 player files work, you a file consists of players and the teams they were on in the year you're starting, as well as players who will enter the league in later years. I'm working on a 1960 file, so it'll have players from 1960 and the teams they are on. So we're going to need to include everyone who was active in 1960 or later -- Pro Football Reference gives me 888 of these players. Here's a little background on Approximate Value -- Approximate Value | Pro-Football-Reference.com -- feel free to quibble about how good of a measure this is, but I'm not aware of any other composite statistics that attempt to compare players in a way that's specific to the individual, removing the rest of the team. If anyone wants to throw out other ideas, though, feel free. Anyway, here's the Top 25 QBs according to AV: Player AV From To G GS AV Tom Brady 326 2000 2022 335 333 326 Drew Brees 277 2001 2020 287 286 277 Peyton Manning 271 1998 2015 266 265 271 Brett Favre 259 1991 2010 302 298 259 Aaron Rodgers 243 2005 2024 248 241 243 Fran Tarkenton 233 1961 1978 246 239 233 Philip Rivers 218 2004 2020 244 240 218 Dan Marino 216 1983 1999 242 240 216 Matt Ryan 210 2008 2022 234 234 210 Ben Roethlisberger 208 2004 2021 249 247 208 John Elway 206 1983 1998 234 231 206 Russell Wilson 188 2012 2024 199 199 188 Matthew Stafford 180 2009 2024 222 222 180 Eli Manning 169 2004 2019 236 234 169 Steve Young 168 1985 1999 169 143 168 Joe Montana 166 1979 1994 192 164 166 Dan Fouts 163 1973 1987 181 171 163 Warren Moon 163 1984 2000 208 203 163 Ken Anderson 160 1971 1986 192 172 160 John Hadl 146 1962 1977 224 166 146 Johnny Unitas 145 1956 1973 165 145 145 Len Dawson 143 1957 1975 192 158 143 Carson Palmer 141 2004 2017 182 181 141 Vinny Testaverde 141 1987 2007 233 214 141 Cam Newton 140 2011 2021 148 144 140 I'm going to guess the formatting is not the best, sorry for that. I think you get the idea. Anyway, you've got the GOAT on top, so I guess we're doing okay there. Brees and Manning at 2 and 3 expose a couple issues we've got, though. First, QBs are better now than before. Or, teams are more QB dependent than before. I think I'm okay with this happening -- I'm not trying to see "who's better regardless of era", I'm just trying to make an FOF9 player file that works, and I think having better QBs as time goes on will allow the league to evolve. Open to other thoughts here, though. 2nd, Brees ahead of Manning? I'm chalking this up to games played. AV is cumulative, in that the more you play, the higher your score will be. Brees played 287 games, and Manning played 266. But Brees has an AV of 277, and Manning was 271. Manning's AV per game was over 1, and it was less than 1 for Brees. So let's look at AV per game. I'm going to give 1 point per game started, and half a point per game not started. Our new Top 25: Joe Milton 1 2024 2024 1 0 1 2.00 Tony Robinson 1 1987 1987 1 0 1 2.00 Norm Van Brocklin 17 1950 1960 12 12 17 1.42 Jayden Daniels 20 2024 2024 17 17 20 1.18 Lamar Jackson 114 2018 2024 103 94 114 1.16 Patrick Mahomes 123 2017 2024 112 112 123 1.10 Steve Young 168 1985 1999 169 143 168 1.08 Jalen Hurts 75 2020 2024 77 66 75 1.05 Roger Staubach 127 1969 1979 131 114 127 1.04 Peyton Manning 271 1998 2015 266 265 271 1.02 Josh Allen 112 2018 2024 111 110 112 1.01 Mike Hohensee 2 1987 1987 2 2 2 1.00 Stephen McGee 2 2010 2011 3 1 2 1.00 Mike Hold 1 1987 1987 2 0 1 1.00 Brian McClure 1 1987 1987 1 1 1 1.00 Mark Stevens 1 1987 1987 2 0 1 1.00 Aaron Rodgers 243 2005 2024 248 241 243 0.99 Tom Brady 326 2000 2022 335 333 326 0.98 Brock Purdy 37 2022 2024 40 36 37 0.97 Drew Brees 277 2001 2020 287 286 277 0.97 Fran Tarkenton 233 1961 1978 246 239 233 0.96 Cam Newton 140 2011 2021 148 144 140 0.96 Daunte Culpepper 98 1999 2009 105 100 98 0.96 Russell Wilson 188 2012 2024 199 199 188 0.94 Trent Green 110 1997 2008 120 113 110 0.94 Your new best QBs of all time, Joe Milton and Tony Robinson! We've got some other issues here, too. Norm Van Brocklin. So, Pro Football Reference only starts calculating AV in 1960. He was the MVP in all four major publications that year -- List of NFL Most Valuable Player awards - Wikipedia. He retired that year, as the league's MVP and winning the NFL Championship. Badass. 9 Pro Bowls in 12 seasons, and his first season he looks not to have played much. So 9 of 11? Also, it seems unfair to take just his only MVP season, and use that to completely base his metric on. But his 1960 stats don't seem crazy out of line with the rest of his career. Season Age Team Lg Pos G GS QBrec Cmp Att Cmp% Yds TD TD% Int Int% Lng Y/A AY/A Y/C Y/G Rate Sk Yds Sk% NY/A ANY/A 4QC GWD AV Awards 1949 23 RAM NFL 8 0 32 58 55.2 601 6 10.3 2 3.4 51 10.4 10.88 18.8 75.1 111.4 69 2 1 1950 24 RAM NFL QB 12 6 5-1-0 127 233 54.5 2061 18 7.7 14 6.0 58 8.8 7.69 16.2 171.8 85.1 103 1 1 PB 1951 25 RAM NFL QB 12 2 1-1-0 100 194 51.5 1725 13 6.7 11 5.7 81 8.9 7.68 17.3 143.8 80.8 65 0 0 PB 1952 26 RAM NFL QB 12 6 6-0-0 113 205 55.1 1736 14 6.8 17 8.3 84 8.5 6.10 15.4 144.7 71.5 97 1 1 PB 1953 27 RAM NFL QB 12 12 8-3-1 156 286 54.5 2393 19 6.6 14 4.9 70 8.4 7.49 15.3 199.4 84.1 61 1 2 PB 1954 28 RAM NFL QB 12 11 6-4-1 139 260 53.5 2637 13 5.0 21 8.1 80 10.1 7.51 19.0 219.8 71.9 80 1 0 PB 1955 29 RAM NFL QB 12 12 8-3-1 144 272 52.9 1890 8 2.9 15 5.5 74 6.9 5.06 13.1 157.5 62.0 106 4 3 PB 1956 30 RAM NFL QB 12 4 2-2-0 68 124 54.8 966 7 5.6 12 9.7 58 7.8 4.56 14.2 80.5 59.5 15 0 0 1957 31 RAM NFL QB 12 12 6-6-0 132 265 49.8 2105 20 7.5 21 7.9 70 7.9 5.89 15.9 175.4 68.8 119 1 1 1958 32 PHI NFL QB 12 12 2-9-1 198 374 52.9 2409 15 4.0 20 5.3 91 6.4 4.84 12.2 200.8 64.1 46 2 1 PB 1959 33 PHI NFL QB 12 12 7-5-0 191 340 56.2 2617 16 4.7 14 4.1 71 7.7 6.79 13.7 218.1 79.5 72 1 3 PB 1960 34 PHI NFL QB 12 12 10-2-0 153 284 53.9 2471 24 8.5 17 6.0 64 8.7 7.70 16.2 205.9 86.5 10 96 3.40 8.08 7.11 5 4 17 PB,AP-1 12 Yrs 140 101 61-36-4 1553 2895 53.6 23611 173 6.0 178 6.1 91 8.2 6.58 15.2 168.7 75.1 10 929 3.40 7.81 7.11 19 17 17 17 Game Avg 17 12 61-36-4 189 352 53.6 2867 21 6.0 22 6.1 91 8.2 6.58 15.2 168.7 75.1 113 2 2 2 RAM (9 Yrs) 104 65 42-20-3 1011 1897 53.3 16114 118 6.2 127 6.7 84 8.5 6.73 15.9 154.9 74.7 715 11 9 PHI (3 Yrs) 36 36 19-16-1 542 998 54.3 7497 55 5.5 51 5.1 91 7.5 6.31 13.8 208.3 75.7 10 214 3.40 7.23 7.11 8 8 17 Jayden Daniels -- Obviously we're not putting him in the conversation for best QB ever, even if we're okay with better QBs. But who the hell knows what to do with a number like that from a rookie? I asked ChatGPT for advice, and here's what I got: Setting a minimum game threshold (e.g., only consider QBs with at least 50 starts)? Incorporating peak performance (e.g., highest single-season AV or best 5-year stretch)? Weighting postseason success (e.g., Super Bowls, playoff wins)? Comparing to era-adjusted stats (e.g., passer rating+, adjusted net yards per attempt)? Minimum game threshold is a no-brainer, but the question is what it should be. Obviously above 3 to get rid of a lot of the dudes in my top 25 from above. But 50 is too high -- I don't want to eliminate Norm Van Brocklin or Jayden Daniels just because I don't like their outlier-ish ratings. I'm going with 12, since it allows me to include Van Brocklin. I don't like the idea of incorporating peak performance. I feel like I want to include a player's ups and downs. Also, getting a player's top 5 years would require some manipulation, that I could probably slog through for QBs, but doing it for all positions would be too much. Not interested in weighting postseason success -- too team-based, and we're trying to get at how good the player is individually. Not interested in era-adjusted stats as I said before, and also not interested in the other stats mentioned that don't remove the rest of the team as much. I told GPT I like the idea of a 12-game minimum threshold, and I said I was still looking for something to reward longevity. The response: Blending career AV with per-game factor: e.g., (AV * weight) + (AV per game * weight), where weight balances impact vs. longevity. Bonus for career milestones: Extra points for reaching 100, 150, 200 games started. Peak + longevity mix: A blend of best 5-year AV and total career AV. I like the idea of blending, but not the other two. GPT suggested this formula: Score=(AV×0.6)+(AV/G×100×0.4) to give a 60% weight to total AV and a 40% weight to AV/G. Here's what we get: Player AV From To G GS AV AV/G GPT Tom Brady 326 2000 2022 335 333 326 0.98 234.64 Drew Brees 277 2001 2020 287 286 277 0.97 204.87 Peyton Manning 271 1998 2015 266 265 271 1.02 203.43 Brett Favre 259 1991 2010 302 298 259 0.86 189.93 Aaron Rodgers 243 2005 2024 248 241 243 0.99 185.55 Fran Tarkenton 233 1961 1978 246 239 233 0.96 178.23 Philip Rivers 218 2004 2020 244 240 218 0.90 166.83 Dan Marino 216 1983 1999 242 240 216 0.90 165.45 Matt Ryan 210 2008 2022 234 234 210 0.90 161.90 John Elway 206 1983 1998 234 231 206 0.89 159.04 Ben Roethlisberger 208 2004 2021 249 247 208 0.84 158.35 Russell Wilson 188 2012 2024 199 199 188 0.94 150.59 Steve Young 168 1985 1999 169 143 168 1.08 143.88 Matthew Stafford 180 2009 2024 222 222 180 0.81 140.43 Joe Montana 166 1979 1994 192 164 166 0.93 136.90 Dan Fouts 163 1973 1987 181 171 163 0.93 134.85 Ken Anderson 160 1971 1986 192 172 160 0.88 131.16 Eli Manning 169 2004 2019 236 234 169 0.72 130.17 Warren Moon 163 1984 2000 208 203 163 0.79 129.53 Johnny Unitas 145 1956 1973 165 145 145 0.94 124.42 Cam Newton 140 2011 2021 148 144 140 0.96 122.36 Randall Cunningham 137 1985 2001 161 135 137 0.93 119.23 Len Dawson 143 1957 1975 192 158 143 0.82 118.49 Terry Bradshaw 140 1970 1983 168 158 140 0.86 118.36 Bob Griese 138 1967 1980 161 151 138 0.88 118.18 We're back to Brady, Brees, Manning again. Top 9 are the exact same as the first time, in order. I played around a little, and felt best with an 85% weight toward AV/G. Now we've got: Player AV From To G GS AV AV/G GPT GPT2 Tony Robinson 1 1987 1987 1 0 1 2.00 80.60 170.15 Joe Milton 1 2024 2024 1 0 1 2.00 80.60 170.15 Tom Brady 326 2000 2022 335 333 326 0.98 234.64 131.86 Peyton Manning 271 1998 2015 266 265 271 1.02 203.43 127.41 Drew Brees 277 2001 2020 287 286 277 0.97 204.87 123.73 Norm Van Brocklin 17 1950 1960 12 12 17 1.42 66.87 122.97 Aaron Rodgers 243 2005 2024 248 241 243 0.99 185.55 120.93 Steve Young 168 1985 1999 169 143 168 1.08 143.88 116.74 Fran Tarkenton 233 1961 1978 246 239 233 0.96 178.23 116.62 Lamar Jackson 114 2018 2024 103 94 114 1.16 114.69 115.48 Brett Favre 259 1991 2010 302 298 259 0.86 189.93 112.23 Patrick Mahomes 123 2017 2024 112 112 123 1.10 117.73 111.80 Philip Rivers 218 2004 2020 244 240 218 0.90 166.83 109.27 Dan Marino 216 1983 1999 242 240 216 0.90 165.45 108.58 Russell Wilson 188 2012 2024 199 199 188 0.94 150.59 108.50 Matt Ryan 210 2008 2022 234 234 210 0.90 161.90 107.78 Roger Staubach 127 1969 1979 131 114 127 1.04 117.67 107.17 John Elway 206 1983 1998 234 231 206 0.89 159.04 106.21 Joe Montana 166 1979 1994 192 164 166 0.93 136.90 104.17 Dan Fouts 163 1973 1987 181 171 163 0.93 134.85 103.17 Jayden Daniels 20 2024 2024 17 17 20 1.18 59.06 103.00 Josh Allen 112 2018 2024 111 110 112 1.01 107.74 102.95 Cam Newton 140 2011 2021 148 144 140 0.96 122.36 102.51 Ben Roethlisberger 208 2004 2021 249 247 208 0.84 158.35 102.49 Johnny Unitas 145 1956 1973 165 145 145 0.94 124.42 101.27 Obviously I haven't applied the minimum game threshold yet, so I guess we can look at Top 23. Whatever. Jalen Hurts and Randall Cunningham are next, if you're curious. Anyway, that's where I'm at now. Manning overtakes Brees for #2, which feels right. Van Brocklin comes in at 4, which I'm fine with, and should make playing in 1960 kind of fun. Obvious skew toward current players, which I'm mostly fine with (seeing Philip Rivers, Dan Marino, Russell Wilson, Matt Ryan, Roger Staubach, John Elway, Joe Montana, Dan Fouts in that order makes me cringe a little though). One issue I still have is that GPT multiplied the AV/G by 100. When I asked about it, here's what I got: The multiplication by 100 is just a scaling factor to keep the two components on a similar scale. Since AV per game is typically a small number (e.g., around 0.9 to 1.2 for most top QBs), without scaling, the second term would be much smaller than the first and wouldn’t contribute much to the final score. That makes sense, but I feel like the 100 number is kind of a tossed-out guess, and I'd rather do something that is more specific to the averages for each stat. Maybe that's dumb, though, because then once I did that, I'd play around with the weights and end up getting something that looks pretty much like this anyway. |
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02-23-2025, 03:34 PM
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Grizzled Veteran
Join Date: Nov 2004
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All I got is I hope you make this work because I’d love to play it.
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02-23-2025, 07:26 PM
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Go Reds
Join Date: May 2001
Location: Bloodbuzz Ohio
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How far down this final list is Burrow?
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02-23-2025, 07:27 PM
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Go Reds
Join Date: May 2001
Location: Bloodbuzz Ohio
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DOLA
I was going to suggest you hit up GPT. It's great for creative little tasks like this. Fun project, I'll keep following along or chime in if I have any ideas. |
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02-24-2025, 09:06 AM
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Thanks! I actually feel good about these ratings the way they are now, so if there's no suggestions by the time I'm done with the files, I'm fine keeping things just as they are. But if people have ideas how I can improve anything, it's helpful. I'll keep you posted on how it goes! Right now, I've got a decent 1960 QBs file with 57 players in it -- that's how many QBs threw a pass in 1960, according to Pro Football Reference. That's not enough for FOF9 -- there's 21 teams in 1960, so you need at least 63 to fill the rosters of each team. It looks like the game has 104 QBs, so I'll need to add another 47. My plan is to go through old drafts and find some guys who were drafted but never played to fill out the rest. The other thing I need to do to fill out the player attribute fields. Right now, they are all -1, which means that their ability in that category is determined by using the overall rating. But putting better values in here will give each player more character as opposed to only being valued by how good they are. Then I need to create the file for the rest of the players. I'm hoping that even though it's a lot more players, I'll be able to just go position by position, and it'll be smoother once I've already done it for QBs. |
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02-24-2025, 09:25 AM
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#6 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Joe Burrow comes in at #55. That probably doesn't tell you much given that this list is not era-adjusted, and really rewards players in the passing era. Here's the Top Ten of players entering the league in 2019-2021 for perspective: Player AV From To G GS AV AV/G GPT GPT2 Jalen Hurts 75 2020 2024 77 66 75 1.05 86.96 100.41 Kyler Murray 77 2019 2024 82 82 77 0.94 83.76 91.37 Joe Burrow 63 2020 2024 69 69 63 0.91 74.32 87.06 Justin Herbert 70 2020 2024 79 79 70 0.89 77.44 85.82 Justin Fields 41 2021 2024 50 44 41 0.87 59.49 80.30 Tua Tagovailoa 49 2020 2024 64 62 49 0.78 60.51 73.46 Jordan Love 30 2021 2024 42 33 30 0.80 50.00 72.50 Easton Stick 4 2020 2023 6 4 4 0.80 34.40 68.60 Trevor Lawrence 41 2021 2024 60 60 41 0.68 51.93 64.23 Gardner Minshew II 35 2019 2024 59 46 35 0.67 47.67 61.92 The last number is my ratings as I have them now. In FOF9 terms, I've got Hurts with a 7, Murray and Burrow with a 6, Herbert and Fields with a 5. These are some interesting numbers. Fields being so high doesn't make sense, but maybe what AV is trying to say is that he was okay while the Bears were bad? Jordan Love being so low is interesting, too. I'm wondering if he's getting dinged by the 9 games he played but didn't start. I just checked, and it looks like he threw a pass in every game he played but didn't start, so it's not like he was just a holder or something. Hurts has some games he played but didn't pass, but he did at least have a rushing attempt every game he played. Either way, I think giving awarding half a game for each game played but not started is way too much. Since we're talking Bengals, Carson Palmer is a weird case. Pro Football Reference has him starting in 2004 even though he was drafted in 2003, because he didn't play at all in 2003. There's probably all sorts of players like that, but I'm guessing none as high-profile as him. |
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02-24-2025, 09:36 AM
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#7 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
GPT has been massively helpful for this. Besides talking through developing formulas like I mentioned, I've used it to help get information that's available out there somewhere, and put it into a table format, so I can paste it into Excel. Starting with the initial 57, I've been able to get jersey number, height, weight, birthdate, birth city, college, year drafted, which pick they were drafted, original team, and year they signed with their current team, which all go into the player file. It's definitely wonky, and can't handle being asked for too much at once, but for the most part, I have it at a point where I don't see glaring errors. I know some draft picks are wrong, but they're close enough. Some of the fields I wasn't able to get. I asked it for hand size and arm length, and it kept telling me it would take a few hours and let me know, but it just kept saying it would be just a few hours more for days. I was able to get it to give me estimates for the "play percentage" and "hall of fame points" field, and it gave me salary info for every player I asked it for. No idea if it's correct, but it at least seems reasonable. Like I said a couple posts up, I still need to add values for the player attribute fields. Things like Touch, Quality, Arm Strength, Scramble, etc. I've played with this a little, and it's given me numbers that seem reasonable in how well they fit the players (mostly looking at scramble and seeing running QBs with higher numbers here). It's frustrating working with GPT, but still fun! |
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02-24-2025, 10:01 AM
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#8 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Playing around with counting a non-started game as 10% of a game. Here's your new top 25
Player AV From To G GS AV AV/G GPT GPT2 Tony Robinson 1 1987 1987 1 0 1 10.00 400.60 850.15 Joe Milton 1 2024 2024 1 0 1 10.00 400.60 850.15 Mike Hold 1 1987 1987 2 0 1 5.00 200.60 425.15 Mark Stevens 1 1987 1987 2 0 1 5.00 200.60 425.15 Dave Lewis 5 1970 1970 14 0 5 3.57 145.86 304.32 Shawn Moore 1 1992 1992 3 0 1 3.33 133.93 283.48 Joe Germaine 1 1999 1999 3 0 1 3.33 133.93 283.48 Jim Sorgi 5 2004 2009 16 0 5 3.13 128.00 266.38 Rich Campbell 2 1981 1984 7 0 2 2.86 115.49 243.16 Jeff Brohm 2 1996 1997 8 0 2 2.50 101.20 212.80 Mike Machurek 1 1984 1984 4 0 1 2.50 100.60 212.65 Ricky Turner 1 1988 1988 4 0 1 2.50 100.60 212.65 Travis Brown 1 2000 2003 4 0 1 2.50 100.60 212.65 Matt Simms 1 2013 2014 4 0 1 2.50 100.60 212.65 Tom Kennedy 3 1966 1966 6 1 3 2.00 81.80 170.45 Harry Theofiledes 1 1968 1968 5 0 1 2.00 80.60 170.15 David Fales 1 2016 2019 5 0 1 2.00 80.60 170.15 Sam Adkins 2 1977 1981 11 0 2 1.82 73.93 154.85 Stephen McGee 2 2010 2011 3 1 2 1.67 67.87 141.97 Ray Brown 2 1960 1960 12 0 2 1.67 67.87 141.97 Jeff Lewis 2 1996 2000 12 0 2 1.67 67.87 141.97 Chon Gallegos 1 1962 1962 6 0 1 1.67 67.27 141.82 Harold Stephens 1 1962 1962 6 0 1 1.67 67.27 141.82 Nate Sudfeld 1 2017 2022 6 0 1 1.67 67.27 141.82 Tom Brady 326 2000 2022 335 333 326 0.98 234.74 132.06 Okay, here's the real one: Player AV From To G GS AV AV/G GPT GPT2 Tom Brady 326 2000 2022 335 333 326 0.98 234.74 132.06 George Blanda 128 1950 1975 224 83 128 1.32 129.53 131.25 Peyton Manning 271 1998 2015 266 265 271 1.02 203.49 127.54 Drew Brees 277 2001 2020 287 286 277 0.97 204.93 123.85 Steve Young 168 1985 1999 169 143 168 1.15 146.95 123.28 Norm Van Brocklin 17 1950 1960 12 12 17 1.42 66.87 122.97 Aaron Rodgers 243 2005 2024 248 241 243 1.01 186.02 121.91 Lamar Jackson 114 2018 2024 103 94 114 1.20 116.45 119.21 Fran Tarkenton 233 1961 1978 246 239 233 0.97 178.68 117.57 Brett Favre 259 1991 2010 302 298 259 0.87 190.12 112.63 Roger Staubach 127 1969 1979 131 114 127 1.10 120.11 112.35 Patrick Mahomes 123 2017 2024 112 112 123 1.10 117.73 111.80 Philip Rivers 218 2004 2020 244 240 218 0.91 167.07 109.78 Joe Montana 166 1979 1994 192 164 166 1.00 139.41 109.49 Dan Marino 216 1983 1999 242 240 216 0.90 165.57 108.84 Russell Wilson 188 2012 2024 199 199 188 0.94 150.59 108.50 Matt Ryan 210 2008 2022 234 234 210 0.90 161.90 107.78 John Elway 206 1983 1998 234 231 206 0.89 159.22 106.60 Jalen Hurts 75 2020 2024 77 66 75 1.12 89.71 106.26 Johnny Unitas 145 1956 1973 165 145 145 0.99 126.46 105.59 Randall Cunningham 137 1985 2001 161 135 137 1.00 122.03 105.18 Dan Fouts 163 1973 1987 181 171 163 0.95 135.71 105.00 Cam Newton 140 2011 2021 148 144 140 0.97 122.78 103.41 Josh Allen 112 2018 2024 111 110 112 1.02 107.89 103.27 And the Joe Burrow cohort (he checks in at #65 now): Player AV From To G GS AV AV/G GPT GPT2 Jalen Hurts 75 2020 2024 77 66 75 1.12 89.71 106.26 Kyler Murray 77 2019 2024 82 82 77 0.94 83.76 91.37 Joe Burrow 63 2020 2024 69 69 63 0.91 74.32 87.06 Justin Herbert 70 2020 2024 79 79 70 0.89 77.44 85.82 Justin Fields 41 2021 2024 50 44 41 0.92 61.37 84.29 Jordan Love 30 2021 2024 42 33 30 0.88 53.40 79.72 Tua Tagovailoa 49 2020 2024 64 62 49 0.79 60.91 74.31 Gardner Minshew II 35 2019 2024 59 46 35 0.74 50.60 68.15 Trevor Lawrence 41 2021 2024 60 60 41 0.68 51.93 64.23 Daniel Jones 45 2019 2024 70 69 45 0.65 53.05 62.10 |
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02-24-2025, 10:14 AM
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#9 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
This is probably a good time to clarify this. From the FOF9 text file that explains these: 0 - The quarterback is of replacement quality. Expect low ratings across the board. 1 - The quarterback is of backup quality. Ratings are generally low. 2 - The quarterback is of starting quality. Ratings can vary. 3 - The quarterback is usually one of the better starters in the league. 4-7 - Increasingly rare - quarterbacks are often All-Star level. 8 - The quarterback is often the best in the league. 9 - The quarterback is a generational talent. 10 - The quarterback is below replacement quality, and will only be placed on a team at the start of a new universe if he's needed to fill a roster. I looked at the 2024 players file that comes with the game, and got these numbers: Row Labels Count of PLAYERID 0 805 34.28% 304 1 572 27.86% 247 2 377 15.36% 136 3 158 6.44% 57 4 73 5.36% 48 5 48 4.29% 38 6 20 3.21% 29 7 15 2.14% 19 8 10 0.71% 6 9 1 0.36% 3 10 (blank) 376 0.00% 0 Grand Total 2455 Sorry about the formatting. The percentages don't line up with the numbers. That's because I also used the distribution of the 2024 quarterbacks file, and took whichever percent was highest. Then for whatever reason, I decided I didn't want any 10s, since I want everyone I create to get in the game. I dunno if that makes sense, but whatever. Anyway, the last column is that percent times 888, since thats how many QBs are in my file. So there will be three 9s, six 8s, and so on down, until everyone left is a 0. George Blanda is obviously interesting here -- he goes from not in the top 25 and a 6 to #2 and a 9, based on his 83 starts in 224 total games. Blanda is a hard guy to rate, but he's pretty awesome, so I'm okay with him being up there. |
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02-26-2025, 01:40 PM
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#10 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Some of the fun of going through this is finding interesting players I've never heard of, or interesting things about players I have. Today we have Ronnie Knox.
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02-28-2025, 08:01 PM
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#11 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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We're moving on to Running Backs!
Using the same methodology that we did for QBs: Player AV From To G GS AV AV/G GPT GPT2 Dave Meggett 52 1989 1998 146 12 52 2.05 113.09 181.82 James White 42 2014 2021 95 13 42 1.98 104.45 174.70 Ronnie Harmon 81 1986 1997 181 27 81 1.91 125.02 174.53 Danny Woodhead 43 2009 2017 101 14 43 1.89 101.57 167.46 Darren Sproles 78 2005 2019 183 31 78 1.69 114.33 155.21 Jim Brown 101 1961 1965 70 70 101 1.44 118.31 137.79 Ladell Betts 36 2002 2010 111 14 36 1.52 82.36 134.51 James Starks 29 2010 2016 76 13 29 1.50 77.50 132.07 Don McCauley 55 1971 1981 156 25 55 1.44 90.74 130.95 Amp Lee 36 1992 2000 116 15 36 1.43 78.97 127.31 Steve Sewell 29 1985 1991 94 12 29 1.44 74.83 126.38 Brian Mitchell 49 1990 2003 223 16 49 1.34 82.81 120.84 Leon Washington 34 2006 2014 126 14 34 1.35 74.37 119.78 Moe Williams 32 1996 2005 132 12 32 1.33 72.53 118.13 Laurence Maroney 27 2006 2010 49 17 27 1.34 69.67 117.66 Lenvil Elliott 32 1973 1981 101 16 32 1.31 71.44 115.82 Jerick McKinnon 36 2014 2023 116 18 36 1.29 73.40 115.47 Mewelde Moore 33 2004 2012 122 15 33 1.28 71.16 114.09 Essex Johnson 50 1968 1976 112 32 50 1.25 80.00 113.75 Lydell Mitchell 100 1972 1980 111 84 100 1.15 106.14 113.04 Marshall Faulk 164 1994 2005 176 156 164 1.04 139.92 112.83 Kevin Faulk 70 1999 2011 161 47 70 1.20 89.95 112.38 Tiki Barber 122 1997 2006 154 109 122 1.07 116.20 109.67 Terry Kirby 55 1993 2002 110 39 55 1.19 80.72 109.66 LaDainian Tomlinson 158 2001 2011 170 155 158 1.01 135.18 109.51 I suspected that the factor of 0.1 for a backup would not work for other positions. Maybe 0.5 is better? Player AV From To G GS AV AV/G GPT GPT2 Jim Brown 101 1961 1965 70 70 101 1.44 118.31 137.79 Marshall Faulk 164 1994 2005 176 156 164 0.99 137.92 108.58 LaDainian Tomlinson 158 2001 2011 170 155 158 0.97 133.69 106.35 Barry Sanders 149 1989 1998 153 151 149 0.98 128.61 105.67 Lydell Mitchell 100 1972 1980 111 84 100 1.03 101.03 102.18 Edgerrin James 135 1999 2009 148 135 135 0.95 119.16 101.35 Walter Payton 167 1975 1987 190 184 167 0.89 135.92 100.96 Billy Sims 63 1980 1984 60 58 63 1.07 80.51 100.21 Terrell Davis 80 1995 2001 78 77 80 1.03 89.29 99.74 Larry Brown 92 1969 1976 102 84 92 0.99 94.77 97.89 Tiki Barber 122 1997 2006 154 109 122 0.93 110.31 97.16 Priest Holmes 92 1997 2007 113 82 92 0.94 92.94 94.01 Gale Sayers 65 1965 1971 68 65 65 0.98 78.10 92.83 Tony Dorsett 137 1977 1988 173 152 137 0.84 115.92 92.21 O.J. Simpson 116 1969 1979 135 129 116 0.88 104.75 92.10 Ricky Watters 122 1992 2001 144 142 122 0.85 107.33 90.82 Chuck Foreman 88 1973 1980 109 84 88 0.91 89.28 90.71 William Andrews 74 1979 1986 87 72 74 0.93 81.63 90.22 Emmitt Smith 169 1990 2004 226 219 169 0.76 131.78 89.91 Christian McCaffrey 83 2017 2024 95 88 83 0.91 86.08 89.55 Lawrence McCutcheon 86 1972 1981 109 82 86 0.90 87.62 89.44 Franco Harris 136 1972 1984 173 162 136 0.81 114.08 89.41 Alvin Kamara 89 2017 2024 115 84 89 0.89 89.18 89.38 Thurman Thomas 136 1988 2000 182 160 136 0.80 113.41 88.00 Maurice Jones-Drew 86 2006 2014 126 71 86 0.87 86.52 87.11 |
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02-28-2025, 08:14 PM
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#12 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Shoot, it looks like I started in 1961 instead of 1960. I don't think it would change anything -- we'd have another season of Jim Brown, but it wouldn't change his ending numbers much. But I'll have to fix my spreadsheet.
One issue I foresee is deciding whether or not to keep FBs as FBs for old players. Pro Football Reference labels Jim Brown, Franco Harris, John Riggins, and Jim Taylor as FB. I'm assuming that's going to kill them in FOF. |
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03-01-2025, 05:35 AM
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#13 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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I added in 1960. Also, I realized I still have players who started less than 11 games filtered out. While that makes sense for QBs, there may be some players here who are in backup roles and don't have 11 starts, but still may be good. Here's what I get when I remove that filter and replace it with 11 games:
Player AV From To G GS AV AV/G GPT GPT2 Jim Brown 122 1957 1965 82 82 122 1.49 132.71 144.76 Jahmyr Gibbs 27 2023 2024 32 7 27 1.38 71.58 121.74 Marshall Faulk 164 1994 2005 176 156 164 0.99 137.92 108.58 LaDainian Tomlinson 158 2001 2011 170 155 158 0.97 133.69 106.35 Barry Sanders 149 1989 1998 153 151 149 0.98 128.61 105.67 Bucky Irving 12 2024 2024 17 3 12 1.20 55.20 103.80 Lydell Mitchell 100 1972 1980 111 84 100 1.03 101.03 102.18 Edgerrin James 135 1999 2009 148 135 135 0.95 119.16 101.35 Walter Payton 167 1975 1987 190 184 167 0.89 135.92 100.96 Billy Sims 63 1980 1984 60 58 63 1.07 80.51 100.21 Bobby Mitchell 43 1958 1967 40 38 43 1.10 69.90 100.17 Terrell Davis 80 1995 2001 78 77 80 1.03 89.29 99.74 Larry Brown 92 1969 1976 102 84 92 0.99 94.77 97.89 Tiki Barber 122 1997 2006 154 109 122 0.93 110.31 97.16 Priest Holmes 92 1997 2007 113 82 92 0.94 92.94 94.01 Gale Sayers 65 1965 1971 68 65 65 0.98 78.10 92.83 Tony Dorsett 137 1977 1988 173 152 137 0.84 115.92 92.21 O.J. Simpson 116 1969 1979 135 129 116 0.88 104.75 92.10 Ricky Watters 122 1992 2001 144 142 122 0.85 107.33 90.82 Chuck Foreman 88 1973 1980 109 84 88 0.91 89.28 90.71 William Andrews 74 1979 1986 87 72 74 0.93 81.63 90.22 Jim Taylor 97 1958 1967 109 109 97 0.89 93.80 90.19 Emmitt Smith 169 1990 2004 226 219 169 0.76 131.78 89.91 Christian McCaffrey 83 2017 2024 95 88 83 0.91 86.08 89.55 Lawrence McCutcheon 86 1972 1981 109 82 86 0.90 87.62 89.44 Obviously that's no good. I'm going to make it so a game played but not started is worth 0.9 games. I think that helps normalize someone like Gibbs, who's benefiting way too much from being considered the backup. It might make sense to just go by games instead of starts, but this might help out some guys who were legit backups at the beginning or end of their careers. Here we go: Player AV From To G GS AV AV/G GPT GPT2 Jim Brown 122 1957 1965 82 82 122 1.49 132.71 144.76 Barry Sanders 149 1989 1998 153 151 149 0.98 128.41 105.24 Marshall Faulk 164 1994 2005 176 156 164 0.94 136.10 104.71 LaDainian Tomlinson 158 2001 2011 170 155 158 0.94 132.31 103.40 Walter Payton 167 1975 1987 190 184 167 0.88 135.47 100.00 Terrell Davis 80 1995 2001 78 77 80 1.03 89.08 99.29 Billy Sims 63 1980 1984 60 58 63 1.05 79.94 99.00 Edgerrin James 135 1999 2009 148 135 135 0.92 117.81 98.47 Bobby Mitchell 43 1958 1967 40 38 43 1.08 69.02 98.28 Lydell Mitchell 100 1972 1980 111 84 100 0.92 96.93 93.49 Larry Brown 92 1969 1976 102 84 92 0.92 91.93 91.84 Gale Sayers 65 1965 1971 68 65 65 0.96 77.40 91.36 O.J. Simpson 116 1969 1979 135 129 116 0.86 104.12 90.76 Ricky Watters 122 1992 2001 144 142 122 0.85 107.14 90.41 Jim Taylor 97 1958 1967 109 109 97 0.89 93.80 90.19 Emmitt Smith 169 1990 2004 226 219 169 0.75 131.40 89.11 Tony Dorsett 137 1977 1988 173 152 137 0.80 114.27 88.69 Tiki Barber 122 1997 2006 154 109 122 0.82 105.84 87.66 Franco Harris 136 1972 1984 173 162 136 0.79 113.25 87.65 Christian McCaffrey 83 2017 2024 95 88 83 0.88 85.01 87.26 Charley Taylor 39 1964 1966 41 41 39 0.95 61.45 86.70 Curtis Martin 131 1995 2005 168 166 131 0.78 109.83 86.01 Priest Holmes 92 1997 2007 113 82 92 0.84 88.68 84.96 Thurman Thomas 136 1988 2000 182 160 136 0.76 111.86 84.69 William Andrews 74 1979 1986 87 72 74 0.87 79.02 84.67 Jahmyr Gibbs checks in at #27, and he would be like #43 if we just counted all games regardless of if he started. Last edited by Passacaglia : 03-01-2025 at 05:35 AM. |
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03-01-2025, 08:45 AM
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#14 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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One thing that jumped out to me was Emmitt Smith at #16. My first thought was that this confirms what I've always thought as a Lions fan in the 90s, that Barry was way better, and Emmitt just looks better because of his O-line. But I also wondered how much he's getting dinged for how long he stayed in the league -- his 15 years is way more than anyone else on this list, and guys who retired early, like Barry, Jim Brown, Terrell Davis, and Billy Sims are up high -- how much worse would those guys look with 15 year careers?
I thought about taking a best 5 seasons and seeing what that would look like. Stathead (Pro Football Reference's pay version) lets me gather data from individual season, but (very very stupidly) limits your results to 200 records per page. I've been able to slog through the copy-and-paste, go to next page, copy-and-paste" deal for 1000 QBs this way, and 2000 RBs, and figure I can do however many it takes for the rest of the positions, but having to go through all seasons that way...it's too much. I did find a filter that lets you include only the first x seasons of a player's career. That seems like what I'm going for, although maybe that might give too much weight to a player's first season or two when they weren't as good or possibly a backup. Here's what I've got using just the first 10 seasons: Player AV From To G GS AV AV/G GPT GPT2 Jim Brown 122 1960 1965 82 82 122 1.49 132.71 144.76 Marshall Faulk 154 1994 2003 146 141 154 1.06 134.74 113.07 LaDainian Tomlinson 153 2001 2010 156 154 153 0.98 131.08 106.42 Barry Sanders 149 1989 1998 153 151 149 0.98 128.41 105.24 Edgerrin James 134 1999 2008 141 135 134 0.95 118.58 101.23 Terrell Davis 80 1995 2001 78 77 80 1.03 89.08 99.29 Emmitt Smith 142 1990 1999 155 153 142 0.92 121.89 99.27 Billy Sims 63 1980 1984 60 58 63 1.05 79.94 99.00 Bobby Mitchell 43 1960 1967 40 38 43 1.08 69.02 98.28 Walter Payton 132 1975 1984 146 140 132 0.91 115.51 96.97 O.J. Simpson 112 1969 1978 122 121 112 0.92 103.95 94.90 Lydell Mitchell 100 1972 1980 111 84 100 0.92 96.93 93.49 Tony Dorsett 126 1977 1986 145 133 126 0.88 110.65 93.38 Larry Brown 92 1969 1976 102 84 92 0.92 91.93 91.84 Gale Sayers 65 1965 1971 68 65 65 0.96 77.40 91.36 Ricky Watters 122 1992 2001 144 142 122 0.85 107.14 90.41 Jim Taylor 97 1960 1967 109 109 97 0.89 93.80 90.19 Adrian Peterson 106 2007 2016 123 116 106 0.87 98.27 89.57 Thurman Thomas 127 1988 1997 154 154 127 0.82 109.19 89.15 Curtis Martin 127 1995 2004 156 154 127 0.82 108.81 88.34 Tiki Barber 122 1997 2006 154 109 122 0.82 105.84 87.66 Franco Harris 115 1972 1981 140 131 115 0.83 102.07 87.52 Christian McCaffrey 83 2017 2024 95 88 83 0.88 85.01 87.26 Charley Taylor 39 1964 1966 41 41 39 0.95 61.45 86.70 Priest Holmes 92 1997 2007 113 82 92 0.84 88.68 84.96 Jahmyr Gibbs is at #31. Emmitt Smith moves up from #16 to #7. Edgerrin James moves up from #8 to #5. Faulk passes Barry at #2, and pulls away pretty significantly. Walter Payton has a big drop from #5 to #10 -- he was the only one on here who had more than 10 years but dropped. His AV/G went up, but it was offset by the 15% weight in his total AV, which dropped. Worth noting that he no longer includes the 1985 season, where he was #2 in MVP voting (to Marcus Allen, himself a riser here from #39 to #27). Last edited by Passacaglia : 03-01-2025 at 08:46 AM. |
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03-01-2025, 03:01 PM
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#15 |
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n00b
Join Date: Mar 2024
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Great stuff. I'm curious to see how the final result will compare to nilodor's files. I do have a few comments and questions:
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03-02-2025, 01:30 PM
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#16 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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If you're curious:
23. Cam Newton 24. Josh Allen 76. Deshaun Watson The first two get a 7 in my files at the moment. Watson gets a 5. The good news for him is that he played 54 games in his 3.5 good season, and only 19 games in his 3-4 seasons since. His AV is 63, in 72 starts and 73 games total. Here's the QBs that entered the league in 2016-2018. There's a few players in there with similar peaks and valleys. Player AV From To G GS AV AV/G GPT GPT2 Lamar Jackson 114 2018 2024 103 94 114 1.20 116.45 119.21 Patrick Mahomes 123 2017 2024 112 112 123 1.10 117.73 111.80 Josh Allen 112 2018 2024 111 110 112 1.02 107.89 103.27 Dak Prescott 112 2016 2024 122 122 112 0.92 103.92 94.83 Jared Goff 113 2016 2024 134 134 113 0.84 101.53 88.63 Deshaun Watson 63 2017 2024 73 72 63 0.87 72.75 83.72 Baker Mayfield 81 2018 2024 106 103 81 0.78 79.96 78.80 Carson Wentz 68 2016 2024 98 94 68 0.72 69.61 71.43 Nick Mullens 17 2018 2024 33 20 17 0.80 42.12 70.39 Mitchell Trubisky 43 2017 2024 78 57 43 0.73 54.90 68.29 Taylor Heinicke 21 2017 2024 42 29 21 0.69 40.32 62.06 For attributes: Yeah, one of the things I'm hoping to do that will improve on nilodor's files is to get rid of the -1 values he has here. Here's what the documentation says: "If not -1, this field should range from 0-250. Be careful, as the distribution of quarterback ratings can change how the game plays, considerably. The highest values should be reserved only for the very best quarterbacks in the league. Most quarterbacks should range from 0-100 in most categories." I'm curious about your simulations -- what did you use for normal attribute stats as opposed to -1s across the board? I'm still working on using GPT to fill these in for me. Here's a sample of what it gave me for the TOUCH rating: Quarterback TOUCH Rating Notes Joe Montana 225 Elite short-pass accuracy, West Coast maestro. Drew Brees 240 Extremely precise, one of the most accurate ever. Tom Brady 230 Surgical with short passes, high completion %. Peyton Manning 235 Master of timing and precision passing. Johnny Unitas 210 Great accuracy for his era, quick release. Brett Favre 180 Strong but aggressive, touch was inconsistent. Michael Vick 140 Cannon arm, but lacked soft touch on short passes. Fran Tarkenton 195 Good short accuracy, but often played off-script. Terry Bradshaw 160 Big arm, but more of a deep passer than a touch thrower. Chad Pennington 245 Possibly the best touch passer ever, lacked arm strength. These were the first values GPT chose to assign, so maybe that means they're the highest values? It took a couple days, but eventually it said it finished assigning it for all the QBs, but then it kept telling me the tool it uses to work with spreadsheets and data processing is temporarily unavailable. It volunteered to just display its ratings at 100 QBs a pop, but then my free limit ran out, so I'll try again tomorrow. |
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03-02-2025, 01:33 PM
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#17 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Also, I wanted to give it props for the high score on Chad Pennington. It really makes me feel like it's putting some real thought into it.
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03-03-2025, 09:34 PM
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#18 |
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n00b
Join Date: Mar 2024
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I didn't add any attribute ratings that weren't already there in nilo's files. I just made a copy of his 1970 file and set all the attributes to -1.
Just for reference, nilodor's files have the following columns filled out for non-filler quarterbacks:
I created two universes, one for the "negative" (one) file and one using nilodor's original file (what I call the "positive" file), and simulated both from 1970 to 2016. I'm unfortunately unable to upload screenshots right now, for some reason, and I wasn't able to OCR the screenshots I took, but there is a statistical trends page in FOF9 and a couple things stood out to me when comparing both universe's trends:
Last edited by KingZal : 03-03-2025 at 09:34 PM. |
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03-03-2025, 09:46 PM
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#19 |
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n00b
Join Date: Mar 2024
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I would love to see accurate attribute ratings for all players, if you can pull it off. My preference, personally, is for the outliers to be well represented. Even if you decide not to get granular with every column (I doubt anybody cares for granular kickoff attributes), I like seeing unique players have their best traits represented well, whether it's Stafford's arm talent, Hester's return ability, Barry's elusiveness, etc. But I don't want to be too nitpicky about how attributes should be done until you're further into that work.
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07-02-2025, 06:50 AM
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#20 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
I decided I'd try this again, by brushing up on my python -- actually vibe-coding with AI, since I don't know python at all. I got a script to pull the data on all QBs from 1920-2024 that didn't take too long, and one to pull the rest of the positions that took five hours last night. It has one row per player per season, for 108,159 rows in the non-QB file, and 5,278 rows in the QB file. All I have is Games, Games Started, and AV, and even AV doesn't exist until 1960. Not many players before then anyway, but I'm going to press on since I'm planning to start a dynasty in 1920. |
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07-02-2025, 12:05 PM
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#21 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Already encountered my first problem with the run, that might make me have to do it again. For my non-QB run, I left all the positions selected, but removed QB. That also caused me to remove players for whom the position was blank. I don't immediately see how to pull just blanks, and if I can't, I'll just have to rerun the whole thing and separate the QBs manually.
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07-10-2025, 09:56 PM
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#22 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
As I'm looking into this again, this factor was still bugging me. So here's what I've done to get rid of the arbitrary 100 number. I kept the AV/G field the same as before -- total AV divided by GS + 0.1 * (G - GS). So if a QB plays in a game but didn't start, it counts as 10% of a game. I also used total AV itself. Then I came up with a Z-score for both of these fields. For AV, that worked great. Tom Brady is way above everyone with a 326 total, Brees is 2nd place with 277. Brady gets a 1.000 and Brees is 0.998. Manning's 271 puts him in 3rd at 0.997. The AV/G field get wonky. I didn't have a threshold here, so Joe Milton and Tony Robinson are on top again. Lamar Jackson has the best Z-score of anyone in contention at 0.878. Using this rewards longevity too much IMO. So I used these Z-scores for AV/G, and took the Z-scores of them. That evened it up a bit -- Lamar Jackson's 0.878 became a 0.970, although I still feel like longevity is weighted too much.  Although it looks like AV/G is enough for Manning and Rodgers to pass Brady. Some data notes: Last time I did this, I was eyeing the creation of a 1960 players file for FOF9 -- this time I'm considering one for 1920. So there are a lot of players with 0 for everything, since AV isn't calculated from 1920-1959. I don't think it will really throw things off much. Also, last time I talked a lot about Norm Van Brocklin and George Blanda -- these guys and everyone else who played both before and after 1960 will be skewed down here, since their G and GS from before 1960 are counting, but they are only getting AV scores from 1960 on. So for example, George Blanda had 340 Games, 106 Games Started from 1949 to like 1975, but his total AV of 128 only covers the games from 1960 to the end. He still comes in at #23, despite a good chunk of his career getting a 0. He's also getting extra points from kicking duties, too, so it's all just a mess. Van Brocklin looks like a nobody, his 17 AV in 1960 puts him way behind in longevity, and it doesn't even look good at an AV/G level since all his Games from before 1960 are counted in the denominator. He's right behind Koy Detmer. |
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07-10-2025, 10:06 PM
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#23 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Burrow drops to #99, and still behind Herbert, but it's close.  Burrow has a higher AV/G than Herbert, though, so it seems like it's injuries holding him back. I still find Fields's ranking interesting -- having him in this company shows that he belongs as an NFL starting QB. |
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07-10-2025, 10:12 PM
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#24 | ||
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Quote:
Now we have: 19. Cam Newton 31. Josh Allen 107. Deshaun Watson 129. Carson Wentz I assume Allen will pass Newton in a couple years -- his AV/G is higher, and if he plays 34 games he should have no problem passing him in total AV, too. |
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07-13-2025, 12:13 PM
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#25 | |
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n00b
Join Date: Mar 2024
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Quote:
Like I said earlier I was curious to see where Watson would rank, since everything I've read tells me his 2024 (half-)season was historically bad. So not surprised to see him dinged here. |
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07-13-2025, 12:14 PM
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#26 |
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n00b
Join Date: Mar 2024
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What do you plan on doing with these files when you finish? Are you going to make individual players files by year like nilodor did, or just two big files for 1920?
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07-14-2025, 08:41 AM
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#27 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Deshaun Watson had an AV of 16 in each of 2018, 2019, and 2020. There's been a little over 200 QB seasons that were 16 or more. So very good seasons, but not historically good. Even in those three years, there were 17 QB seasons that were 16 or more, including a 22 from Mahomes in 2018 and a 25 from Lamar Jackson in 2019. And Watson was indeed bad in 2024, his AV was 0. But historically bad? Nah. Check out Dan Pastorini, who in 1981 had an AV of -6. 2TDs, 14 INTs. Dan Pastorini 1981 Game Log | Pro-Football-Reference.com There's also a couple more recent examples of a -5. 2021 Mike Glennon, 4 TDs, 10 INT. Mike Glennon 2021 Game Log | Pro-Football-Reference.com There's also 2012 Ryan Lindley, 0 TDs, 7 picks. Ryan Lindley 2012 Game Log | Pro-Football-Reference.com Watson's 5 TDs and 3 INTs in 2024 seem downright good compared to these guys. |
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07-14-2025, 08:47 AM
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#28 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Right now, I'm making two big files for 1920, for use in my dynasty here: 1920 Racine Street Cardinals -- A No APFA FOF9 Dynasty - Front Office Football Central Once these files are done, creating files for a later year isn't *that* much work. It's enough work that I'm not going to create 100+ of these if no one wants them, but quick enough that I wouldn't mind creating them for anyone with a specific request. |
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07-16-2025, 07:08 AM
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#29 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Moving on from QBs. For those who don't know FOF9 file structure, there's two files for players -- QBs are in one file, since they have more attributes, and all the other players are in the other file. So...here's the rest of the positions.
When calculating AV/G for QBs, I made a non-started game equal to 0.1 of a game. That felt right for QB in terms of getting numbers how they should be, and of what made sense -- a QB who's coming off the bench might be mop-up duty, might be coming in at some random point in the game due to injury, might be a kick holder or something weird like that. For other positions, I'm thinking they may be subbing in often as a backup, maybe a situational player, so I upped the value of a non-started game to 0.5.  This is all players who started 1960 or later, but I couldn't resist adding Jim Brown to compare with the rest, even though he started in 1957. I fudged the numbers a little and assumed his first three seasons had a similar AV to the rest of his career. This might shortchange him a little. He had two MVP seasons in those first three years, third place in another. Despite that, his AV/G of 1.49 absolutely kills everyone else -- the next highest are Billy Sims at 1.07 and Lawrence Taylor at 1.05. All these other really amazing players are bunched around 1, and he's at almost 1.5. Insane. I feel like longevity may be overvalued here, but that probably works when looking at the top. The only current players on here are Aaron Donald and Bobby Wagner. I'm fine with that -- you can't really talk about how a player compares with all-time greats until their careers are done. We talked about Billy Sims being #2 in AV/G, but he doesn't even make the top 25 due to longevity. Does he belong? He played 5 seasons, so it's not like he just got hot for a bit or something. But it's still hard to create a top players of all-time list and say he's better than Lawrence Taylor. I dunno. |
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07-17-2025, 07:18 AM
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#30 | |
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n00b
Join Date: Mar 2024
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Quote:
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07-17-2025, 08:34 AM
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#31 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Me too! This is pretty much transitioning now from using AV to getting the files ready. I'm doing a couple things now: 1. Looking for dupes. There's some oddities in the data I have. In the large data pull I ran from PFR, there's an "age" field for each player's season. I've been using that instead of finding the birthdate, at least for now. Jason Campbell, for example, gets his age screwed up a few times because his birthday is December 31. And some players who had different positions listed in different seasons snuck through my attempt to make sure a player is only listed at one position. I had to manually go through about 80 players from 1920-1959 to find out if they were the same player listed twice, or two different players with the same name. Weird trivia -- there have been only two NFL players named Bob Sullivan, and both of them played in 1948. Same with the name John Perko, two players, but both played in 1946. 2. Filling blank positions. A lot of early players don't have a position listed in the big data pull. At first, I just put in a random position, but now I'm running a python script that looks at the PFR and wikipedia page for that player and tries to find a position. Also, the early years are heavily weighted toward offense, likely because they were all two-way players listed at just their offensive position. I'm going through players listed with a / (something like LT/LDT) and deciding that if it's close overall, I'll go with the defensive position to help even it out. It probably won't even it out, but it will help.  Next steps after that: 1. Look at RB and FB. It will be a waste for Jim Brown to be listed at FB, and probably a lot of other players too. The FOF instructions say that 2 is the top value that can be assigned to FB. I've found that not to be the case, that you *can* give a FB a rating higher than 2, but maybe the point is that it doesn't make him any better than if he were a 2. My plan is to take all the FBs that I've rated higher than 2 and just change their position to RB. 2. Fill attributes. I don't have great ideas here yet. My current plan is to just ask AI something like "hey give me the top 50 QBs in terms of touch" and go from there. I don't think I'll get a solution where I can have a rating for every QB, but I figure if I can fill in some noted outliers, a little bit will go a long way. 3. Fill in fake players. The early years are pretty light overall, and especially light in defense and QB -- I only have 10 or 11 in 1920 now. I'm fine rolling with that, and even though I'm using 32 teams, I'm okay with only 10 or 11 having "good" QBs to start. But I'll need to add about 170 QBs rated 10 (below replacement quality), otherwise the game will start generating fake players who will be good. I'm also toying with the idea of having QBs who joined the league throughout the 20s just start in 1920, but then that means there's none left to be drafted. Maybe I'll just have some of them move their start year up, and some stay back. |
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07-18-2025, 10:56 AM
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#32 |
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Go Reds
Join Date: May 2001
Location: Bloodbuzz Ohio
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If you're looking for opinions on 3, Pass, I'd say I would prefer you keep the years entered as close to real life as possible, despite the challenges of depth at QB. For instance, add those below replacement quality guys if it means there are 10 good QBs to start, and even if a few drafts are light on QB, users will just know that's the beginning challenges of a 1920's start.
Last edited by korme : 07-18-2025 at 10:56 AM. |
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07-19-2025, 09:07 PM
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#33 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Thanks Shorty! I think that makes a lot of sense, too. In some ways, it might not even be a challenge, but a benefit knowing you just have to get one of the 10 good QBs. But I guess you might not get the chance.
On the other hand, I'm looking at a guy like George Munns. George Munns Pro Football Stats, Position, College, Draft, Transactions This guy's first season in the NFL was in 1921, for the Cincinnati Celts. I don't know what he did in 1920 -- although the Cincinnati Celts weren't in the NFL in 1920, but they did have a team, so if he was on the team, he wouldn't have been "in the NFL" yet. In 1919, he played on the Akron Pros, who joined the NFL in 1920. He feels like a guy I could say started in 1920. He may be the only one like that, but there are other guys like Ralph Jones, who played in 1 game in 1921, at the age of 41. Probably wouldn't be appropriate to put him in 1920, but wouldn't be crazy to just get him in the league at 1920 either. I'm with you, though -- I'd rather just stick to what's here and roll with it. I look at guys like Munns and think about fiddling, but there's a question of how much individual research to do, too. Meanwhile, I'm getting more bothered by the uneven distribution between offense and defense. It'd be one thing if there were less players overall, but it feels wrong to have 73 players just at RB and FB, when there are 74 players on defense total. So some of those 73 won't even get to start, while I'm way short on defense. I'd love ideas here if anyone has any. |
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07-20-2025, 05:43 PM
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#34 |
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n00b
Join Date: Mar 2024
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Is that 74 defensive players for 1920 alone, or the entire decade? I like the idea of generating filler quarterbacks and just having to run with less talent in a primitive era, but the thought of doing that with defense gives me pause.
I think you should try that regardless and see how the game plays, or at least what the stats look like over time. I'm okay with having weaker, wonky stats that far back in a dynasty - in fact, I've kicked around the idea of neutering 20th century quarterbacks to simulate the offensive explosion of the 21st century - but I imagine that it might not be fun to play a universe where the early pickings (on one side of the ball) are so slim. Last edited by KingZal : 07-20-2025 at 05:43 PM. |
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07-20-2025, 05:48 PM
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#35 | |
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Hall Of Famer
Join Date: Nov 2000
Location: Behind Enemy Lines in Athens, GA
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Quote:
I'm assuming this is an issue at least in part because of 2-way players?
__________________
"I lit another cigarette. Unless I specifically inform you to the contrary, I am always lighting another cigarette." - from a novel by Martin Amis |
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07-21-2025, 09:44 AM
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#36 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
That's just for 1920. If you look at post #31, that's got the breakdown by position. The positions are on top, but might be hard to spot. When the league starts in 1920, there'd be 247 players on offense, and 74 on defense. Not so bad, but it gets worse as time goes on. In 1921, we add 171 on offense and 57 on defense. So now we're looking at 418 on offense to 131 on defense (it's actually worse than this because this is just the players file, QBs are not included). My plan is to go through some of the best players in the league, and do some research on whether or not they were best known for offense or defense -- probably they were just super good at both, but we'll see. Then I'll try to get some of the guys rated 1 or 2 and split them, figuring no one will care. I've been thinking about nerfing past eras like you mentioned, too. My thought has been that since AV is not era-adjusted, using it will do a pretty good job of that for me. However, since I'm only using AV for players who started in 1960 or later, it won't really be nerfing the era from 1920-1959. I'm hoping that the ranking list from that era will effectively do it, by not having many QBs ranked highly. Here's the breakdown of ratings by decade for QBs:  I feel pretty good about that. We've got 8 guys rated 8 or 9 in the 1980s or later, and 4 before that. The one 9 from the 1930s is Dutch Clark, who is actually listed as RB in the rankings, but I changed him to QB because he looks like a great dual threat. I like that there are way more players rated 4+ later on, too. Maybe some of that is due to the fact that I'm using percentages and there are more players in later years, but I think this will work well. Last edited by Passacaglia : 07-21-2025 at 09:45 AM. |
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07-21-2025, 10:02 AM
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#37 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Exactly -- most guys are listed as something like End or Back or Tackle, which was probably meant to cover both their offensive and defensive position. But even guys who are listed as RB or QB that feel definitively offensive also would have played defense. Here's a breakdown of positions I have currently for 1920:  When translating these to modern/FOF positions, these are all things I interpreted as offense. LE/RE are WR, so I randomly assigned them one of FL or SE. BB is FB. LH, RH, TB, and WB are RB. Some of the blanks likely get filled in during later seasons. For each player, I took the most common position he was listed at, so if one of those blanks plays in a later season and gets listed at a position, it gets listed. I still haven't entered in the results of the script I ran to check wikipedia for positions yet. That will help with the blanks, but probably not the offense/defense issue, I assume they'll all get position names that sound like they play offense. I think I'm going to have to just take some of these players and randomly put them on defense. If they're low-rated, they're probably not special enough that anyone is going to froth at the mouth "hey that guy's a RB!" if I put him at LB -- it's more important to make sure all these guys are in the game and playing, instead of having them as backups on offense while fake players play defense. |
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07-22-2025, 09:32 AM
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#38 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Okay, I think I have a solution to the offense/defense issue.
I made a list of all the possible positions. There's 354, because there are things like G, LG, RG, LG/RG, RG/LG, and BB-TB-WB-FB, and so on. Each position gets a score from 0 to 1 based on how "offense" it is:  Most of your normal positions are all 1 or 0, but E and B are 0.5 since they are likely two-way players. Not too fancy -- I just eyeballed each position and gave it either 0, 0.25, 0.5, 0.75, or 1. FWIW BB-TB-WB-FB is 0.5, RDT/RT/MG is 0.25, FB-BB-WB is 0.75. I'm assuming those don't come up too often so I'm not sweating it. Next step is to create an "offensive percentage" for each player's career. For each season, take their total offensive score from above, and divide by total number of seasons. Then, for each cohort of players based on their first season in the league, put them in order based on offensive percentage, and make the top half offense, and the top half defense. This part was a bit funky -- for example in 1920, 199 of the 350 players had were 100%. I could have done something like use total of number of seasons to figure out which 175 were "more" 100%, but this seemed like a good natural break, and a split of 199-151 isn't so bad.  Not the 50/50 that I wanted, but I'll take it. The issue I'm having now is that when I use AI to assign positions, what's happening is that all the E are on defense -- so there are no TEs, 1 FL, 1 SE, and a ton of DEs. I'm going to look into that, and maybe experiment with refining the T position to have a 0.75 offensive score and see what that does. |
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07-22-2025, 09:47 AM
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#39 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Also, I wanted to look into this. The most offensive player assigned to defense is https://en.wikipedia.org/wiki/Pettis_Norman , who played 11 seasons at TE, but 1 season at RE, but I guess 1962 needed some defense. And the most defensive player assigned to offense is Dale Hellestrae - Wikipedia , who played 14 seasons as a long snapper after a rookie year at Tackle.
I'm mostly fine with Hellestrae, but Pettis Norman is a bit of a shame. Maybe there needs to be some thresholds based on the actual percentage instead of going completely by rank. |
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07-23-2025, 08:51 AM
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#40 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Some good news! I found some stats NFL Stats that go back to 1920. I've put the stats on this site for 1920-1959 in passing, rushing, receiving, interceptions, punting, and kicking into csv files. That should be useful to help me identify who really belongs in each specific position for guys listed as Back or End.
Looking at the passing stats, there are 66 players that aren't included in my file. Some of them are nickname-based, like these stats have Hopalong Cassady, and I have him in it as Howard Cassady, and some are abbreviation based -- this site calls him Y. A. Tittle and my file calls him Y.A. Tittle. Next steps with these stats: 1. Find any names of players I don't have and add them in. 2. Get these players ranked by number of pass attempts, rush attempts, receptions, interceptions, and kicks. Whatever that player's best rank is, that's his position. If he doesn't rank in anything, he's probably defense. |
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07-24-2025, 10:22 PM
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#41 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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So I was working on this stuff for a while, then switched to my work, and it looked exactly the same -- a spreadsheet with a bunch of data. I guess that says something about something.
Anyway, things are looking good! I only added three players to my data: Eagle Feather - Wikipedia Arrowhead (American football - Wikipedia) And a guy who I can't find any other info on other than that his name is "Seymour" and that he had 1 reception for 3 yards for the Cardinals in 1940. I feel like I have a good read on who to label as QBs though!  The first column for each player here is their best rank in terms of pass attempts, 2nd column is their best rank in rush attempts, 3rd column is best rank in receptions, 4th column is interceptions. I'm using pass attempts here because I'm not trying to determine how good they are as a QB, I'm trying to determine how often they were used like one. This gives me 30 QBs in 1920, and 19 that will come in for 1921. |
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07-25-2025, 09:09 AM
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#42 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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One thing I should have noted is that since this doesn't include kicking and punting yet, some of these players who look like QBs now might end up looking like Ks or Ps when I'm done. There are punt return stats too, but since that's not a position on its own, I'm only going to use them for help determining the punt return attribute.
Biggest surprise is probably Jim Thorpe. I guess that's bound to happen, since he's the only player here whose name I knew before doing this. I always thought he was more of a running back, but in 1920, he threw 24 times, 4th most in the league, and ran only 18 times. Some of this may be age-related -- he was 33 in 1920, and maybe if the league had started earlier, we would have seen him crack the top 3 in rushing attempts. I'm open to changing him to RB if people think that makes sense. |
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07-25-2025, 09:50 AM
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#43 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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So this process is taking longer than it should because of the matching for lookups. Neither pro football reference nor this jt-sw are consistent about using nicknames vs. real names, or there's just disagreement on spelling. Stuff like Charley Trippi vs. Charlie Trippi. Or Curley Morrison -- when that doesn't match, my first assumption is the other site has him listed as Curly, but no, he's Fred. My favorite has to be a guy listed as Dick Halladay on that site, but when you search wikipedia, there's no Dick or Richard or Richie Halladay -- that's because wikipedia and pro football reference list him as Death Halladay - Wikipedia -- oh, and his name is actually Robert. A lot of time spent, but it will all be worth it if I can get Death Halladay on my FOF team.
Last edited by Passacaglia : 07-25-2025 at 09:51 AM. |
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07-25-2025, 02:21 PM
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#44 |
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Go Reds
Join Date: May 2001
Location: Bloodbuzz Ohio
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Loving this project, Pass. Keep up the good work.
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07-26-2025, 10:15 AM
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#45 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Thanks Shorty! I know there may not be a lot of interesting things to say, but I appreciate the chatting about it!
Here's the process I've got going now: I took the old stats I found, and created the rankings that pretty much look like what I had above. I added kicking and punting stats, too. Then, I applied the following rules in order to all players from 1920-1959 (I'm assuming by 1960 this stuff get less murky): 1. If you were Top 10 in pass attempts in any season, you're a QB. This may have weirdness given that the number of teams ranges from 8 to 22 during this period, but at this point, I don't know which season I'm looking at anyway. 2. If you were Top 30 in rushes, receptions, or interceptions, you're either a RB, WR, or Defense, depending on which is lowest. This rule is in place to prevent guys who played a real position and kicked from being assigned as a Kicker or Punter. 3. If you weren't in the Top 15 in Kicking or Punting, it doesn't count. This rule is in place to prevent guys who are OL or DL and don't have any stats from being assigned Kicker or Punter just because they did it a few times. 4. If you haven't been assigned a position yet, you get assigned QB, RB, FL/SE, DEF, K, or P based on whichever ranking is lowest. 5. If you've been assigned RB but your position is listed in pro football reference most often as FB, you get assigned FB. 6. If you're assigned DEF or NONE, we take the position that you were listed at most in the pro football reference data and assign you something that makes sense.  7. At this point, almost everyone has a position or a group of positions. For each year, the players are placed in order of how flexible they are -- players who can be one position first, then two, and so on, ending with guys who are 0 -- no position listed in pro football reference ever, and no stats to indicate any position either. They can be anything. 8. We use a python script to assign everyone who can be multiple positions to whatever position they can be that's most needed. Here's the distribution I'm looking for:  I've done steps 1 through 7, and am now working on Step 8. There's a lot to pick apart here, so feedback is welcome! Last edited by Passacaglia : 07-26-2025 at 10:40 AM. |
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07-27-2025, 10:13 AM
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#46 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
I've made some changes to these rules, since I was getting too many players locked in at QB/RB/WR because they had some stats in a category but it clearly was not their primary position. 1. If your best stat was interceptions, you're defense. 2. If you were in the top 20 in passing, rushing, or receiving, you get a position based on the smallest of these. 3. If you don't have a position yet and were in the top 15 for kicking or punting, you get a position based on that. 4. If you don't have a position yet, you get NONE. Then the usual 5 and 6 from before: 5. If you've been assigned RB but your position is listed in pro football reference most often as FB, you get assigned FB. 6. If you're assigned DEF or NONE, we take the position that you were listed at most in the pro football reference data and assign you something that makes sense. Step 6 uses the same lookup tables as before, I might have made small modifications to get more ILBs in. If I recall, the only change I made was that I allowed guys listed by pro football reference as RB to play LB as well as DB.  Looks like I made too many cuts to RB and WR. There's no reason those should be below target since those are the only guys we have stats for. I also suspect that the script is choosing LG over RG, LT over RT, LDE, over RDE etc, whenever there is a tie. Not a huge deal, but I think it should choose randomly from the available positions instead of just going down the list. I'm also considering that maybe I shouldn't bother assigning players to special teams. What's the point of having a player in the game file if I've changed his position to LS, and to a lesser extent P or K? I'm probably going to need to generate a good amount of fake players in those positions anyway, so maybe I should cut my losses and put them somewhere else? I guess that's why I cut kicking and punting stats already -- not because I was getting too many Ks and Ps, but if a guy is a real football player I want him included that way instead of as a K or P. |
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07-27-2025, 11:28 AM
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#47 | |
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Go Reds
Join Date: May 2001
Location: Bloodbuzz Ohio
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Quote:
As someone who probably gets more enjoyment out of modding OOTP's master csv files, I completely understand  |
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07-28-2025, 03:31 PM
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#48 | |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
Nice! It's weird when I tell my wife I'm playing FOF, but what I actually have up is spreadsheets and a command prompt window. It's going to be weird when/if I'm done and I'm actually just playing FOF. Hopefully, if there's an FOF 10, I'll be able to use most of this work for that. |
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07-30-2025, 02:34 PM
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#49 |
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Update:
I started rethinking my distribution be position. At the time I made these, I was concerned with making sure everyone in the league gets a starter ASAP -- but I need to think more long-term than that, since this distribution is going to be applied for 40 years. Instead of trying to make an equal weight of starters, I want to make an equal weight of roster spots. So now I have:  Note that here I've decided not to include any LS in here. The game will make a bunch of fake ones, or maybe I'll add a bunch of fake names to the file. I don't think it makes sense to shortchange myself in other positions so that I can fill up LS. For K and P, the weights are lower now than before, since each of them is 1 out of 52 (instead of 53 since we're taking out LS), instead of 1 out of like 27 or 28 (instead of 24, since I added a bit more for 3rd WR, nickel CB, and 2nd TE). This allows me to keep the position in, not have as many as I had before, and it makes it easier to only use players that make sense (i.e. had kicking stats). New rules for assigning players to a position: Your best rank in each of these categories needs to be in the Top X to count at all: Passing -- 33 Rushing -- 60 Receiving -- 68 Kicking -- 32 For punting and defense, if you have punted even once, or had an interception, you're considered for that category. Then, we take your best rank between passing, rushing, kicking, and defense. If your best rank is passing, you're a QB. If your best rank is rushing, you're an RB or FB (defaults to RB, but if pro football reference thinks you're a FB, then you're a FB). If your best rank is receiving, you're a FL or SE. If your best rank is defense, you're defense. If you still don't have a position, and have a kicking or punting stat, we take your best of those (also, if you ever led the league in kicking or punting, you get that as your position no matter what other stats you have). Here's what I've got:  Looking VERY good. I got a handful extra QBs and RBs than I thought I would because in some years, there are just a lot of DBs already. Based on the limiting numbers I chose for X above, I figured I would not get anyone assigned to QB or RB unless they had the stats for it. We're talking 2 QBs and 10 RBs over 40 years, and it actually does balance things out for that year, so I'm fine with it. TEs are a bit lower than I'd like -- coming in at 5.06% when I wanted 6.73%. The only thing I can think of that makes sense is to have some of those guys with catches play TE instead of WR. But it's tricky to decide who -- I don't think I have a good way to automate that without risking putting someone who obviously should be a WR at TE accidentally. Pretty much everyone who pro football reference calls an End, who doesn't have any catches, or interceptions, is ending up at TE. So while this bugs me a little, I feel like tinkering is only going to make it worse. We may end up with a situation where those TEs are also worse overall than other positions, but at this moment, I'm not seeing a great way to fix it. Currently we're low on RBs and high on FBs. I'm still planning to take any FB rated higher than 2 and switch them to RB, so that should help. After that RB/FB switch, I'm a little shocked to say it, but this may actually be a usable file? |
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07-31-2025, 11:36 AM
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#50 | ||
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Coordinator
Join Date: Oct 2000
Location: Big Ten Country
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Quote:
I really felt like I was just going to leave it, but in the back of my head, I knew that if I ruminated on it for a while, it would bug me too much and I'd want to tinker. I'm deciding to allocate players with receiving stats to either TE, FL, or SE instead of just FL or SE. Right now, I'm getting more TEs than I wanted and less WRs, but I feel like that may be a better fit for this period anyway. Quote:
I've also reworked this. Instead of making sure a player is ranked in the Top X for that season, I'm choosing a number that's different for each season, based on the number of players in the file that year. For example, in 1920, there are 351 players in the file. Old method, you just got your passing stat included if you were in the top 33. But now, X is equal to the number of total players times 0.23 for passing, and something like 0.4 for rushing. So in 1920, your passing counts if you're in the top 80, but in 1928, where there's only 58 players, you have to be in the top 13. That's not how many QBs get in the file -- that number is lower, because some of these guys end up playing other positions due to another stat being better. Using this method, I ended up included your stats in receiving, punting, and kicking no matter what. But at least, this way, random guys who have 1-2 catches but positions in pro football reference of linemen or something end up as TEs instead of WRs. |
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