Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

I generated the following table from testing 40 yard dash times with a stopwatch of the AI with 90 speed 90 acc HB vs 80 speed, 80 acc LB:

<TABLE style="WIDTH: 240pt; BORDER-COLLAPSE: collapse" border=0 cellSpacing=0 cellPadding=0 width=320><COLGROUP><COL style="WIDTH: 48pt" span=5 width=64><TBODY><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 48pt; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19 width=64>Setting</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 48pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" width=64>Threshold</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 48pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" width=64> HB</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 48pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" width=64> LB</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 48pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" width=64> Diff</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19>Varsity </TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.11</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.63</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0.52</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>50</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.02</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.49</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0.47</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: windowtext 0.5pt solid; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" class=xl65 height=19></TD><TD style="BORDER-BOTTOM: windowtext 0.5pt solid; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" class=xl65 align=right>100</TD><TD style="BORDER-BOTTOM: windowtext 0.5pt solid; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" class=xl65 align=right>4.03</TD><TD style="BORDER-BOTTOM: windowtext 0.5pt solid; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" class=xl65 align=right>4.35</TD><TD style="BORDER-BOTTOM: windowtext 0.5pt solid; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" class=xl65 align=right>0.3</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19>Heisman</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.31</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.8</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0.48</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>50</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.3</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.62</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0.325</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>100</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.16</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>4.35</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0.19</TD></TR></TBODY></TABLE>

This tells us several important facts:

1) Players are faster on varsity than heisman regar(AA is in between; not shown here). By adjusting the difficulty level the computer is just improving the attributs of the AI not actually increasing the difficulty of the game. Designers have previously stated that this is how the difficulty works but I wanted to actually show that. They have also previously stated that the exact way it works is to boost the AI and penalize the human on Heismann, no effect on AA, and boost the human and penalize the AI on Varsity:
--Varsity boost attributes of human controlled players
--Varsity likley penalizes AI attributes
--Heismann penalizes human players attributes
--Heismann likely boosts AI players attributes

2) The gap of combine players between HB and LB is about 0.16 seconds to 0.19 seconds. For players in pads and given poorer speed on Defense in college lets make a goal of 0.25. Even on default of Heismann gap is too large this is made much worse on varsity where the AI appears to be much faster at HB relative to LB (perhaps the attribute boost is a % and not an absolute).
--Default threshold is too big but not too bad at Heismann
--Default threshold is much too large on varsity
--Threshold slider appears ot have a similar impact on the % change of speeds around an unknown value at either varsity of heismann.
--Threshold of 0 negatively impacts the speed differential.

3) HB get faster as threshold increases. This is even more noticable with linebackers. This means that the threshold slider does NOT move players closer together around a median/mean value but rather pushes all players statistics towards 99 compressing speed values (i.e. it makes all players <99 faster), conversely a lower threshold makes all playres even those at 90 slower stretching teh difference.
--Threshold shortens the gap or increases the gap around a value of 99 not around a value of 90.
--WR at 99 are not getting slower, CB at 90 are getting faster as threshold approaches 100
--WR at 99 are not getting faster, CB at 90 are getting slower as threshold approaches 0

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

I hope this clarifies how difficulty level and sliders work. I personally would not recommend difficulty of Heismann or Varsity because of the player attribute effects and the difficulty of modifying sliders around these changes, however, if you have a set that you like more power to you. Just be aware this is what is actually happening not a change in AI intellect.

I also do not think a threshold of 0 particularly with the game on varsity is at all realistic but again if you enjoy continue. The default setting of 50 is not bad but I like faster LB and prefer a value of 65 (gives a gap of 0.25 on AA).

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

I thought for sure that the developers had previously stated that the difficulty level affects the rate of decision-making for computer-controlled players. Thus, computer-controlled players will react more quickly (and more often) to what they are "seeing" on Heisman than on Varsity.

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

My test has nothing whatsoever to do with what the AI is seeing it is pure stat testing. This very clearly shows that the AI is recieving attribute boost from the slider settings. The reason they see more is because the boost is from increased awareness, reading plays, and other ratings which effect jumping routes and responding to your players. Thus they do see more, but this is through a general attribute boost.

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

To make this easier to understand here is the expected gap between the LB and the WR in feet at each setting covering a distance of 30yd line to opponents 30 yard line prior to the catch (40yds).

<TABLE style="WIDTH: 144pt; BORDER-COLLAPSE: collapse" border=0 cellSpacing=0 cellPadding=0 width=192><COLGROUP><COL style="WIDTH: 48pt" span=3 width=64><TBODY><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; WIDTH: 144pt; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8; mso-ignore: colspan" height=19 width=192 colSpan=3>Gap between LB and WR</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19>Varsity</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>13.38ft</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>50</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>10.05ft</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>100</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>8.4ft</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19>Heisman</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>0</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>12.12ft</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>50</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>8.43ft</TD></TR><TR style="HEIGHT: 14.4pt" height=19><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; HEIGHT: 14.4pt; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" height=19></TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>100</TD><TD style="BORDER-BOTTOM: #ece9d8; BORDER-LEFT: #ece9d8; BACKGROUND-COLOR: transparent; BORDER-TOP: #ece9d8; BORDER-RIGHT: #ece9d8" align=right>5.22ft</TD></TR></TBODY></TABLE>

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

Hey Man I'm going to jump into this convo with you...

My first discrepancy with your work is that your going off 40 times. In game (IRL as well) NO ONE is running around doing their "40 times". Normally they have 3 chances and their best time is taken down and recorded. Its mostly about, IMO, 'am I faster than you' when athletes compete on the football field. None of this research takes into account what these players run with equipment on as well.

What your doing is great for a starting point but shouldn't be used as the actual foundation you should use for this topic. Think about it, alot of corners/WR in the power conferences run sub 4.5 40 times. Speed is still a determining factor. Those Tebow-led Gator teams were running circles around the rest of the SEC and most of those guys are between 4.2-4.4 speed. Acceleration is the biggest factor in all of this. The lower the speed threshold the better representation of a player's true speed will be displayed in NCAA '11. But I'll let you continue.

I'll continue to monitor your results though. Good work so far.


Ray

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

So with these statistics your basically saying the only way to get the players to truly play to their attributes without the garbage boosts is on All-American thats really interesting.

Re: Varsity vs AA vs Heismann explained along with threshold effects (testing)
 

He actually says he is showing what has been previously stated by game designers, kind of the same thing you are saying but definitely a difference if people wanted to get all tight-britches about it.