From: silber@m.cs.uiuc.edu (Ami A. Silberman)
Subject: Re: Value of Stats
Organization: University of Illinois, Dept. of Comp. Sci., Urbana, IL
Date: Tue, 28 Jul 1992 22:45:11 GMT

91287041@ramsey.cs.laurentian.ca (roger maynard) writes:

>>This is important.  In fact, what is most important is that we can go beyond
>>this a bit, as follows.  Our models predict actual winning and losing up to a
>>certain percent of error that we can't seem to get rid of.  Now, that error
>>is the portion of winning that is not "explained" by the model.  It could have
>>several components: clutch performance, personal intangibles, attitude, team
>>chemistry, sheer random luck.  But, and this is the key, the *sum* of those
>>effects can't be any bigger than the variation from what is predicted that we
>>actually observe.  If the model is invariably correct within 5%, then the sum
>>total of all the luck, chemistry, clutchness, etc. is no more than 5% of the
>>process.

>I agree with this totally except for the 5% figure.  Are you telling me that
>you can predict the Win/Loss of all teams to within 5%?  Before the case?  Not
>likely.  If you really can I would like to know more about it.

No, but what we can do is as follows:
You can, in baseball at least (and, I think, most other long season sports),
calculate a teams winning percentage from the runs scored and runs allowed.
The formula is, if I remember correctly, S^2/(S^2+A^2). Its accurate to within
about 5% about 95% of the time. There are notable exceptions, the '84 Mets
for instance, who posted a winning percentage of over 55% while being outscored.
Its a bit like RC, usually accurate, and when it isn't we can often figure
out major components of why it isn't. (RC is often distorted for teams that
either run a lot, or don't run etc. I suspect that Boston tends to score
fewer runs that RC would have it since with all that team slow (the opposite
of team speed) they have fewer runners scoring on singles. Also teams with
managers addicted to one run strategies or batting Sammy Sosa lead-off 
score fewer runs than predicted by RC. For a league as a whole, though, RC
is pretty accurate.)
Getting back to predicting the number of wins. Teams that either win a lot
more than expected (based on runs for and runs against), or a lot fewer than 
expected, sometimes do so for palpable reasons, some of them clearly
connected with luck. If a team wins a higher than average number of
really lopsided games (15 - 3 and the like) than that can skew the number.
(Note that the good teams tend to win more games of every kind, and 
that, contrary to a lot of folk wisdom, they win the lopsided games. 
Losses of 1 run games tend to be either fairly random or traceable to very
inept relievers (Something I keep facing in my PTP email league).)
So a team that scores 760 runs and gives up 700 should have about an 88-72
record. Suppose the team has a record of 82 - 80 then, a difference of over
five percent. What could have caused it:
1. Let's look at how many blow outs they won as opposed to how many they lost.
Perhaps they were involved in a couple more 18 - 3 games than was normal.
2. Let's look at how their relievers were. This may be reflected in one run
games, may not be. If they started the season with Jim Acker as their closer
and a nincompoop as their manager, they might have ended up losing a lot
of one run games, whereas their wins may have been by more usual margins. 
(I can't remember who posted it or when, but there was a historgram of the
average margin of victory, most games are decided by more than one run.)
3. They may just have had bad luck in one-run games. Look at their winning
percentage in one-run games. In general, teams winning percentages in one-run
games converge on 50%. ie., a team with a 60% winning percentage might win
56% of its 1 run games, a team with a 40% winning percentage might win 45%.
This might, just might, represent intangibles, if a team "plays over its
head" and wins lots of one run games. It might also represent chance.
Suppose we have a team that was 18-5 in one run games, how do we evaluate
if this was due to "intangible factors". First, we might check their actual
run production vs. that given to us by RC. Maybe they were a better team in
terms of hitting etc. than the rest of their record indicates. Secondly,
look for patterns. Do they have Dennis Eckersly? Did they win five of
those games against Jim Acker. Do they have a great defensive catcher
who they bring in to protect a one run lead? 
Third, look at their record in one run games over several years. Chances are
that this 18-5 was a random fluctuation. Teams that exceed their predicted
win total (predicted via the formula above) one year don't tend to the next,
and vice versa. In general, teams that exceed their predicted win total
do worse the next year, exceptions being usually due to an influx of talent
and a maturation of young players (cf. 1985 Mets.) 18-5 is lucky, but
it isn't all that lucky. If a team was 12-13, 18-5, and 7-13 in one run 
games in three consecutive years, with essentially the same personel, then
its likely that the 18-5 year was a fluke. (Unless, of course, it was the
year they had Dan Gladden or some such nonsense.) Occasionaly the addition of
a single player can drastically improve a team, or appear to. What usually
has occured is that the new player did help the team (Gary Carter for the
'85 Mets) by his play (not his club house attitude), but the supporting
cast also had good years, matured some (for instance, '85 was Darlings
first full year if I remember correctly.), and may have had career years.
	So, where does this lead up to? If you give me a random team's runs for
and against, I can "guess" its win-loss record to within about 5% most
of the time. Furthermore, if I get it wrong, I can, if I look at the rest of
their stats (with the exception of pitcher WL records) probably fine tune my
prediction to a bit closer. Furthermore, if I get it wrong still by more than
5% but you give me the runs for and against for the previous and next year,
then I'll probably get within 5% of them, and have an idea of whether I was
high or low for the year you first gave me. (Note, the team has to be chosen
arbitrarily, in the modern era there are well over 1500 team/seasons, and
I'm sure that there are at least ten or twenty with just really weird 
win-loss records just due to chance.)
	So, my challenge to those of you who believe heavily in intangibles.
Before the start of next season, predict not the win-loss records of each team,
but how much they play over their heads (or under their feet.) By how many
wins will they exceed or fall short of the number predicted by runs for and
against. Note, I'm not arguing against all "intangibles". Some things that
are called "intangibles" aren't, because they have very tangible effects
prior to the W-L record. Things like off season conditioning, playing with
nagging injuries (which may have a deleterious effect), drug abuse, 
an ability to suck up to the manager etc. These effect a players OBA, SLG,
etc, and can be treated, in the main, as tangibles. (Sometimes it helps to
know why a player has improved or gotten worse. Perhaps you will trade for
a lesser player based on his youth and minor league experience, perhaps
you know that he really is better than his stats indicate, but is just
screwed up by Tom House or something.) The real hard core partisans of
intangibility say though that it is reflected in something other than
just the number of runs scored, but in the number of wins. Perhaps, but
it seems ephemeral.
	In summary, I've argued against "intangibles" having an undue
influence upon runs scored or games won. By undue influence, I mean an
influence over and above that of the normal consituent parts. You win
games by scoring more runs than you allow. A team that scores 760 runs
and allows 700 in a season normally has a record of about 88-74. 
If a team scores 760 runs and allows 700 and has a record of 90-72, you
can't claim that that record is due to intangibles such as "knowing how
to win, winning the close ones, or winning attitude". Its due to having
scored 760 runs and allowed 700. Perhaps it was the intangibles that
allowed the team to score 760 runs and give up 700? Well, by using RC
formulas, and whatever is equivelent for pitchers, maybe we find that
the team "should" have scored 750 runs and allowed 695. This isn't a
significant difference. You can't talk about "they know how to score
runs, they have really timely hitting, they're great in the clutch,
etc." They had a correct numbers of singles, walks, steals etc. to end
up with 755 runs. (If their predicted RC had been 830, in the other case,
you might look to see whether their manager liked to bunt with the score tied
in the third inning etc., i.e., one run strategies. One run strategies 
sacrafice total number of runs for increased expectation of one run.)
Perhaps their intangibles were responsible for their batting averages,
sluggins averages, OBP etc. Well, how did the players do last year, how
is this team different from last years? Whose new, whose old, who learned
a new pitch, or worked out over the off season. Did the new TWG improve
when he came here? Did the old players? Sometimes when a player improves
unexpectedly it is because of "attitude". The attitude of "I'm not proud,
I'll deign to talk to a hitting coach", or "I'll stop eating two pizzas a
day", or "I'll practice fielding more." I think that most of the time
we can chase the intangibles back to where they interact directly with
actual hard stats, hits, walks, etc.
-- 
ami silberman - janitor of lunacy
	silber@cs.uiuc.edu
Newsgroups: rec.sport.baseball