FOOLmetrics

[kaosfere]

Background: As I mentioned here, I was looking at this seasons' results thinking about what factors might go into a team's performance in a season that would cause it to differ from the Pythagorean expectation.

Toronto was notable, this year, for having a very high difference from expectation of +11 games. When I was looking at my team stats to try to figure out why this might be, I noticed something else interesting: my record in "clutch" games -- one-run and extra inning -- was also notably high (.769 and .739, respectively).

After a bit of thought, this started to make sense to me. Wins or loses in very close games will have a disproportionate effect in pulling your expectation away from the mean: a one-run win will not raise your expected average (EXP%) much, but it will raise your actual average just as much as a 10-run blowout. A team that plays in a lot of close games, then, can be expected to be likely to have a greater differential from their EXP% in reality.

I started to think about this a slightly different way. The typical thought about the Pythagorean model is that it measures a team's "luck". A team that is lucky and performing above its EXP% can be expected to regress towards the mean, the same as a team having an unlucky streak. However, this expectation may break down for teams with a tendency to close games, in which the clutch-factor may make for a bigger win/loss swing in the EXP% than one would expect.

Looking at it this way, I theorize that we can break down a team's EXP% into two different factors: "clutch", the ability to perform well (or poorly!) in tight ball games, and "luck" -- all the other nebulous factors that introduce fuzz to the statistics.

In the table below, I have columns for several statistics that I have invented -- and which all might need more thought and refinement! -- but the most relevant here are the two I would like to submit to you which I have termed (creatively) "CLUTCH" and "LUCK".

I have figured that if you take a team's W/L in one-run games, and subtract from it the team's overall W/L record, you can isolate the portion of their performance that comes from clutch situations. In the tables below, I have taken this one-run W/L minus overall W/L and multiplied it by 1000 to remove the usual percentage figures and give us an easier number to compare and manipulate.

Conversely, if you assume that EXP% is made of two components, then once you remove the clutch component from it you are left with pure luck. (Note that this isn't necessarily "luck" per se, just another, smaller sub-category of things which can alter the outcome of games). The LUCK statistic, then, is what we gate if we take a team's EXP% and subtract the clutch percentage (one run W/L - overall W/L, or CLUTCH/1000) -- then change the exponent on that just like we do for CLUTCH.

(This *1000 may not be necessarily, and may be a hindrance to further calculations, but it made it nicer for me to look at and compare the numbers on a general level.)

By breaking these two figures out, we can take any team's difference from expectations, and determine how much of that was a quantifiable ability, and how much was luck. In particular, if we know compare any two teams' CLUTCH standings, you may have a good idea how a close game between them would come out.

(And you can figure out, with LUCK, who really was the luckiest, and who might have fared even better -- or worse! -- if everyone's luck was the same).

So, anyway... here's a table I have constructed with each team's 1975 performance. Below it, you will find the definitions for all the non-standard columns:

		W	L	AVG	R	RA	EXP%	DOE	ORW	ORL	ORGA	PORG	CLUTCH	LUCK
Chicago		89	65	0.578	756	616	0.601	(0.023)	20	20	0.500	0.260	-78	55
Columbus	85	69	0.552	678	646	0.524	0.028	35	22	0.614	0.370	62	(34)
Boston		80	74	0.519	726	695	0.522	(0.002)	21	26	0.447	0.305	-73	70
Compton		77	77	0.500	621	655	0.473	0.027	23	19	0.548	0.273	48	(21)
Colorado	75	79	0.487	619	620	0.499	(0.012)	26	24	0.520	0.325	33	(45)
Hartford	73	81	0.474	641	685	0.467	0.007	23	29	0.442	0.338	-32	39
Ann Arbor	73	81	0.474	683	711	0.480	(0.006)	19	21	0.475	0.260	1	(7)
New Orleans	64	90	0.416	628	724	0.429	(0.014)	17	23	0.425	0.260	9	(23)
													
Toronto		93	61	0.604	580	544	0.532	0.072	40	12	0.769	0.338	165	(93)
Valdosta	91	63	0.591	629	490	0.622	(0.031)	24	25	0.490	0.318	-101	70
Rio Grande	80	74	0.519	632	627	0.504	0.016	26	24	0.520	0.325	1	15
Atlanta		75	79	0.487	649	625	0.519	(0.032)	23	29	0.442	0.338	-45	13
Texas		72	82	0.468	574	620	0.462	0.006	21	26	0.447	0.305	-21	27
New York	72	82	0.468	577	622	0.463	0.005	29	27	0.518	0.364	50	(45)
Baltimore	68	86	0.442	558	567	0.492	(0.050)	23	36	0.390	0.383	-52	1
Brooklyn	65	89	0.422	553	657	0.415	0.007	24	31	0.436	0.357	14	(7)

DOE: Difference Over Expected (EXP% - AVG)
ORW: One Run Wins
ORL: One Run Losses
ORGA: One Run Game Average (ORW / (ORW + ORL))
PORG : Percentage of One Run Games ((ORW + ORL) / (W + L))
CLUTCH: (ORGA - AVG) * 1000
LUCK: (DOE - (ORGA - AVG)) * 1000

[Young Drachma]

Are you using a spreadsheet? If so, wanna share? FOOLmetrics are seriously the coolest thing ever.

[Young Drachma]

Interesting to see how clutch Toronto was and how lucky they weren't. Really great stuff!

[kaosfere]

I've got more thoughts on this, what it means, and how we can break further information out of it, but I don't have time to work on it at the moment. I definitely think we might be able to use the LUCK factor to find a modifier for teams' performance in order to determine their true potential.

In the meantime, since sometimes it's just fun to have some silly numbers to look at, I humbly present the clutchiest and luckiest teams of this season.

1975's Clutchiest Teams

1. Toronto: 165
2. Columbus: 62
3. New York: 50
4. Compton: 48
5. Colorado: 33

1975's Luckiest Teams

1. Boston: 70
2. Valdosta: 70
3. Chicago: 55
4. Hartford: 39
5. Texas: 27

More to come later. Maybe. Any thoughts, comments, criticisms, or other insights appreciated.

[graygoose12]

One thing I notice:

If I am understanding what you are saying, according to those last two columns, a team is either clutch or lucky. The two aren't independent of each other.

[Young Drachma]

Interesting too that the Top 5 weren't both lucky AND clutch, but one or the other. I like it.

[Alan T]

Something that I would be interested in seeing is how this scales across multiple seasons. Most discussions about how "Clutch" a player or team is using stats always breaks down on trying to carry it over across seasons. Ie: players are very clutch one season and then the next seemingly forget how to be clutch. I believe that is why many people started feeling that difference in the expected pyth record being how lucky a team is or isn't as it was not able to really map it across seasons.

[graygoose12]

Originally Posted by Dark Cloud

Interesting too that the Top 5 weren't both lucky AND clutch, but one or the other. I like it.

I think that this is a problem with the different statistics.

There needs to be a way to make these two independent, because right now you are either clutch or lucky.

It's not a coincidence, but based on the calculation.

[kaosfere]

Originally Posted by Dark Cloud

Are you using a spreadsheet? If so, wanna share? FOOLmetrics are seriously the coolest thing ever.

I do have a spreadsheet. I made this up in iWork, since I'm a Mac geek, but I just ran a quick export to Excel for anyone who wants to work with it. It loads, and looks OK, but I haven't tested any of the formulae there.

http://www.sigilism.com/fool/FOOLmetrics.xls

Good thoughts from Alan and graygoose, thanks. I'll ponder them some more when the wife's not trying to get me moving.

*runs off to do non-FOOL things before he gets in trouble!*

[graygoose12]

I think clutch and luck should somehow add up to the 'DOE'

[Young Drachma]

Sweet. Thanks. Have fun with the honey do list.