The following is my entry for Stat Geek Idol. The contest is run by TeamRankings.com. Check out some of the entries from last year's competition here.
Introduction:
There are 347 teams in Division I college
basketball. The nature of the sport allows for all different kinds of styles of
play. Every team has varying player personnel and coaching philosophy. College
basketball analysts are given the tough task of forecasting the end result of
games featuring contradicting styles. It seems undeniable that, in some cases,
certain teams can be bad matchups for other teams. Still, quite frequently
analysts just say what sounds good. To illustrate this point, let’s look at a
first round matchup from this year’s NCAA tournament:
The four factors (shooting, rebounding, taking care
of the ball, and drawing fouls) are a very good way to assess style of play.
The Minnesota-UCLA matchup featured the best offensive rebounding team in the country (Minnesota) and the 263rd defensive rebounding team in the country (UCLA). A smart analyst would point this compatible Minnesota strength and UCLA weakness out, but what does it really mean for the expected outcome of a game? On one hand, Minnesota should kill UCLA on the offensive boards, possibly creating a huge advantage for Minnesota. On the other hand, Minnesota kills just about everyone on the offensive boards. UCLA wouldn't be able to stop the lethal Minnesota rebounding attack regardless, so maybe this is a waste of an opponent weakness for Minnesota.
Essentially, the question I am asking here is
simply: If you are really good at one of the four factors, would you rather
play a team that is normally good at defending that factor (strength on
strength) or really bad at defending that factor (strength on weakness)? At
first thought, strength on weakness feels like the right choice. The goal of
the following analysis is to try to answer this question.
Part
1: The effects of opponent on each four factor
To begin this study, I compiled a sample size of
every single Division I college basketball game from 2009, 2010, 2011, and
2012. Games from 2013 (up until around the first week of March) were also
included. I wound up with exactly 26,000 games to draw conclusions from.
In order to look at what happens when a {good/bad}
offensive {eFG/TO/OR/FTR} team played a {good/bad} defensive {eFG/TO/OR/FTR}
team, I had to define what exactly good or bad means. I decided that any team in the 90th percentile or better of a given four factor was “good” at that skill and any team in the 10th percentile or worse of a given four factor was “bad” at that skill.
NOTE: Good/bad is just the opposite for defense
(i.e. - you want your opponent’s eFG% to be low)
The next step was to use these definitions of good
and bad to find instances of strengths meeting strengths, weaknesses meeting
weaknesses, and so on in the 26,000 game sample. First, let’s take a look at
what happens when a good shooting team plays a good defensive shooting team:
The above table shows that there were 409 games
where a good offensive eFG% team played a good defensive eFG% team. The offense
averaged an eFG% of 54.5% on the season. However, when they played a good eFG%
defense, that number decreased to 49.8%.
I did this same analysis for all types of matchups
and all the four factors. The results are below:
There is a lot going on here, but the two biggest takeaways are:
1.
Bad vs. bad brings out more good than good vs. good brings out bad. Basically,
when two bad teams at one factor play each other, the offense improves a lot.
When two good teams at one factor play each other, the offense does not
diminish quite as much.
2.
The defense controls FTR the most and
eFG% the least. If you look at the percent change column, an offenses
ability to get to the foul line changed a lot depending on the defense. On the
other hand, an offenses ability to make shots did not change nearly as much.
This is consistent with past research on similar topics.
Part
2: The effects of style on efficiency
Part 1 showed exactly what happens to the individual
four factor based on opponent, but that is only so helpful in determining if
there is evidence for good/bad matchups. The more important thing to look at is
the effects of style on points per possession. Let’s go back to UCLA-Minnesota.
Say UCLA decided that they needed to make an extra effort to keep Minnesota off
the offensive glass. This decision might come at the risk of a different four
factor. Maybe UCLA focusing on defensive rebounding diminishes their ability to
create turnovers. This idea wouldn’t show up in the part 1 results, but it
would show up in points per possession.
To look at the effects of efficiency, I first
calculated an expected points per possession using simply the ORtg (adjusted
for schedule) of the offense and the DRtg (adjusted for schedule) of the
defense. This expected PPP was made without looking at matchups or style of play
at all. Then, the expected PPP could be compared to the actual PPP. If the two
numbers significantly differ, that means that mismatches in four factors can
give us more information on which team will most likely win the game.
As you can see, matchups had virtually no effect on
the actual points per possession of the game. I was able to predict PPP by
simply using the offensive and defensive averages extremely effectively. Here
are the final key takeaways from the tables above.
1.
Four factor matchups don’t increase prediction accuracy.
If we once again go back to Minnesota-UCLA, this means that we shouldn’t have
looked too far into the offensive rebounding advantage. Simply looking at which
team is better efficiency wise is adequate.
2.
FTRate had very little effect on the
points per possession of an offense. If you look at the
Actual PPP column, there is not much change in general. This particular study
indicates the eFG% is the most important four factor, followed by OR%, TO%, and
finally FTR.
Conclusion
It would be foolish to say that specific matchups
have no effect on the outcome of a basketball game. It doesn't mean that
matchups can’t possibly matter just because this study shows no evidence for
it. However, the study does indicate that it may not be wise to focus too much
on the compatibility of the strengths and weaknesses of opponents. Trying to
breakdown strength and weaknesses may be a futile activity. Simply put, the
best way to predict the winner of a game appears to be just picking the better
of the two teams.
