Showing posts with label Sports. Show all posts
Showing posts with label Sports. Show all posts

Friday, December 10, 2010

Each NBA Shot is a Decision: What's the Decision Model?

These days, the sports world is full of people paying close attention to statistics....
  1. General Managers use unconventional statistics to find hidden value in players....
  2. Fans discuss stats when debating what a player is worth, how much playing time he should get and who he should be playing against.
  3. Journalists use statistics to write stories - at best they'll use numbers to create a new narrative; at worst they'll use numbers to conveniently support an intuitive--but false--"story hook"
David Biderman, who writes "The Count," a Sports and Numbers blog for the WSJ, seems to have fallen into the latter category with his recent piece on what the Miam's Heat's passing means for their scoring chances.  His convenient but suspicious "story hook" is that the more the Heat pass, the lower their shooting percentage gets.  Here at Team Fitzgerald, we are deeply skeptical.

Biderman's contention is that because the Heat's shooting % is higher when they pass less, they should start acting more selfishly, pass less, and voila...they can boost their shooting percentage.  If it were that simple....

But wait!...correlation does not equal causation.  Do players get higher percentage shots when they pass less?  Or do they pass less when they have a high percentage shot, for example on a fast break?  

Let's take an important step back:  each pass or shot is a decision.  Professional players make it thousands of times, and coaches monitor how well they make that decision.  We praise players who take higher percentage shots when they have them, and blame them when they give up a good shot with a pass they didn't need to make.  On the other hand, if a player does not have a high percentage thought, we praise the decision to pass...nobody admires a "forced shot" with low probability of hitting the net.

If a player has a sweet fast break, or an open path to basket, the rational choice is to shoot (or dunk) bc they already have their high percentage shot.  If the defense is able to get back, and the player can't easily take it himself, the rational choice is to slow it down and look for a set play that may involve more than 1.  These are the decisions a player makes based on the set of facts in front of them in the moment.  By not eliminating fast break plays, controlling for shot location, etc...Biderman is left with a data sample skewed enough to result in incorrect conclusions.  Separating them would be a better way to see the impact of passing on a given basketball play.


So mathematically, let's run a quick scenario.  During each game hundreds of "pass vs. shoot" decisions will be made.  Rational team players shoot when the odds are in their favor (high percentage shot) and pass when they don't have a good shooting opportunity, seeking a better one.  Meanwhile, the shot clock ticks...and as time passes, the calculus changes.  Given the risk of running out of time on the shot clock, extra passes become less attractive and shots on the basket become more imperative, and players rationally are more willing to take lower-percentage, lower-quality shots.  Shooting percentages can be expected to fall, because players are forced to shoot without having found an easy layup or fast-break dunk. 

The article was based on data from only two games by one team.  We wish we could find a similar but  potentially more valuable data-set:  shooting percentages by teammate, segmented by the number of seconds on the shot clock.  This would allow us to test our hypotheses, which is that game patterns, and the related passage of time, cause patterns in shooting percentage, and that passing less is correlated with, but does not cause, higher shooting percentages.

We feel that Biderman's piece misses is what's at the heart of why statistics in sports are so captivating to so many of us. They allow us to reveal truths that were previously hidden.  Biderman's piece doesn't make an attempt to dig deeper into why the Heat score less when they pass more.  There is no hidden truth in this article, only a potential straw man. 

What do you think?

Alex Roberts & Jaime Fitzgerald

Tuesday, December 15, 2009

The Impact of Money in Sports: Correlation or Causality?

According to the Wall Street Journal's sports metrics column, The Count, the NHL shows the strongest link between team payroll and winning percentage of all professional sports. It's a provocative and counter-intuitive conclusion, as many sports fans assume that baseball is most impacted by financial disparities between big-city teams and other, smaller market competitors.

The column compares the correlation between payroll and winning percentage in the 4 major sports:

  1. NHL shows the highest correlation between payrool and winning: .49
  2. MLB places a close second at .43,
  3. The NBA and NFL lag behind at .24 and .15, respectively.
I'm intrigued, but also skeptical, and most of all curious to see more comprehensive analysis of the dynamics at play here. Glad to see the info, but several "open questions" remain:

  1. Causality: since correlation does not prove causality, it may be premature to conclude (as the column does) that in the NHL "More Dollars Equal More Wins." What if causality runs in the other direction, with winning teams earning more revenue, which they in-turn spend retaining their best players?
  2. What's The Mechanism?: how does extra money generate additional wins? If the NHL is really the league where money buys success most consistently, how is that investment achieved? One could think of this as the "Return on Investment" on professional sports payrolls....
  3. Role of Sabermetrics? -- In most businesses, ROI is higher when investments are better screened and selected. Facts and analysis are used to make better decisions which achieve superior returns. This is where the NHL and MLB are especially different: player-valuation metrics are advanced and heavily used in Baseball, while they are less well developed in the NHL. I'm curious to investigate further how the "metrics environment" affects ability of team owners to "buy wins" in one sport versus another....
  4. Effects of League Financial Structure?: the financial dynamics, salary cap rules, and revenue-sharing arrangements vary significantly amongst the professional sports leagues. What impact might those differences have on the "causal impact" of money in each league?
Posted By:
Jaime FitzgeraldAlex Roberts