Decision Biases: Sports Analytics Series Part 4

One way to look at on-field analytics is that it is a search for decision biases.  Very often, sports analytics takes the perspective of challenging the conventional wisdom.  This can take the form of identifying key statistics for evaluating players.  For example, one (too) simple conclusion from “Moneyball” would be that people in baseball did not adequately value the value of being walked and on-base percentage.  The success of the A’s (again – way oversimplifying) was based on finding flaws in the conventional wisdom.

Examples of “challenges” to conventional wisdom are common in analyses of on-field decision making.  For example, in past decades the conventional wisdom was that it is a good idea to use a sacrifice bunt to move players into scoring position or that it is almost always a good idea to punt on fourth down.  I should note that even the term conventional wisdom is problematic as there have likely always been long-term disagreements about the right strategies to use at different points in a game.  Now, however, we are increasingly in a position to use data to determine the right or optimal strategies.

As we discussed last time, humans tend to be good at overall or holistic judgments while models are good at precise but narrow evaluations.  When the recommendations implied by the data or model are at odds with how decisions are made, there is often an opportunity for improvement.  Using data to find types of undervalued players or to find beneficial tactics represents an effort to correct human decision making biases.

This is an important point.  Analytics will almost never outperform human judgment when it comes to individuals.  What analytics are useful for is helping human decision makers self-correct.  When the model yields different insights than the person it’s time to drill down and determine why.  Maybe it’s a shortcoming of the model or maybe it’s a bias on the part of the general manager.

The term bias has a negative connotation.  But it shouldn’t for this discussion.  For this discussion a bias should just be viewed as a tendency to systematically make decisions based on less than perfect information.

The academic literature has investigated many types of biases.  Wikipedia provides a list of a large number of biases that might lead to decision errors.  This list even includes the sports inspired “hot-hand fallacy” which is described as a “belief that a person who has experienced success with a random event has a greater chance of further success in additional attempts.”  From a sports analytics perspective the question might be asked is whether the hot-hand is a real thing or just a belief. The analyst might be interested in developing a statistical test to assess whether a player on a hot streak is more likely to be successful on his next attempt.  This model would have implications for whether a coach should “feed” the hot hand.

Academic work has also looked at the impact of factors like sunk costs on player decisions.  The idea behind “sunk costs” is that if costs have already been incurred then those costs should not impact current or future decision making.  In the case of player decisions “sunk costs” might be factors like salary or when the player was drafted.  Ideally, a team would use the players with the highest expected performance.  A tendency towards playing individuals based on the past would represent a bias.

Other academic work has investigated the idea of “status” bias.  In this case the notion is that referees might call a game differently depending on the players involved.  It’s probably obvious that this is the case.  Going old school for a moment, even the most fervent Bulls fans of the 90’s would have to admit that Craig Ehlo wouldn’t get the same calls as Michael Jordan.

In these cases, it is possible (though tricky) to look for biases in human decision making.  In the case of sunk costs investigators have used statistical models to examine the link between when a player was drafted and the decision to play an athlete (controlling for player performance).  If such a bias exists, then the analysis might be used to inform general managers of this trait.

In the case of advantageous calls for high profile players, an analysis might lead to a different type of conclusion. If such a bias exists, then perhaps leagues should invest more heavily in using technology to monitor and correct referee’s decisions.

  • People suffer from a variety of decision biases. These biases are often the result of decision making heuristics or rules of thumbs.
  • One use of statistical models is to help identify decision making biases.
  • The identification of widespread biases is potentially of great value as these biases can help identify imperfections in the market for players or improved game strategies.

A Quick Example of the Limitations of Analytics: Sports Analytics Series Part 3.1

In Part 3 we started to talk about the complementary role of human decision makers and models.  Before we get to the next topic – Decision Biases – I wanted to take a moment to present an example that helps illustrate the points being made in the last entry.

I’m going to make the point using an intentionally nontraditional example.  Part of the reason I’m using this example is that I think it’s worthwhile to think about what might be “questionable” in terms of the analysis.  So rather than look at some well-studied relationships in contexts like NFL quarterbacks or NBA players, I’m going to develop a model of Fullback performance in Major League Soccer.

To keep this simple, I’m going to try and figure out the relationship between a player’s Plus-Minus statistic and a few key performance variables.  I’m not going to provide a critique of Plus-Minus but I encourage everyone to think about the value of such a statistic in soccer in general and for the Fullback position in particular.  This is an important exercise for thinking about combining statistical analysis and human insight.  What is the right bottom line metric for a defensive player in a team sport?

The specific analysis is a simple regression model that quantifies the relationship between Plus-Minus and the following performance measures:

  • % of Defensive Ground Duels Won
  • % of Defensive Aerial Duels Won
  • Tackling Success Rate (%)
  • % of Successful Passes in the Opponents ½

This is obviously a very limited set of statistics.  One thing to think about is that if I am creating this statistical model with even multiple years of data, I probably don’t have very many observations.  This is a common problem.  In any league there are usually about 30 teams and maybe 5 players at any position.  We can potentially capture massive amounts of data but maybe we only have 150 observations a year.  Note that in the case of MLS fullbacks we have less than that.  This is important because it means that in sports contexts we need to have parsimonious models.  We can’t throw all of our data into the models because we don’t have enough observations.

The table below lists the regression output.  Basically, the output is saying that % Successful passes in the opponent’s half is the only statistic that is significantly and positively correlated with a Fullback’s Plus-Minus statistic.

Parameter Estimates
Variable DF Parameter
Estimate
Standard
Error
t Value Pr > |t|
Intercept 1 -1.66764 0.41380 -4.03 <.0001
% Defensive Ground Duels Won 1 -0.00433 0.00314 -1.38 0.1692
% Def Aerial Duels Won 1 -0.00088542 0.00182 -0.49 0.6263
 Tackling Success Percentage 1 0.39149 0.25846 1.51 0.1305
% Successful Passes in Opponents 1/2 1 0.02319 0.00480 4.83 <.0001

The more statistically oriented reader might be asking the question of how well does this model actually fit the data.  What is the R-Square?  It is small.  The preceding model explains about 5% of the variation in Fullback’s Plus-Minus statistics.

And that is the important point.  The model does its job in that it tells us there is a significant relationship between passing skill and goal differential.  But it is far from a complete picture.  The decision maker needs to understand what the model shows.  However, the decision maker also needs to understand what the model doesn’t reveal.   This model (and the vast majority of other models) is inherently limited.  Like I said last time – the model is a decision support tool / not something that makes the decision.

Admittedly I didn’t try to find a model that fits the data really well.  But I can tell you that in my experience in sports and really any context that involves predicting or explaining individual human behavior, the models usually only explain a small fraction of variance in performance data.

A Non-Judgmental Analysis of the NFL Rating Decline

Over the last week there has been a lot of discussion regarding the decline in NFL ratings this season.  The facts seem to be that the NFL is experiencing a weakness in prime time games that has resulted in an 11% drop in ratings.  The NFL has circulated a memo that cites a variety of factors such as the presidential campaign.  Notably the memo states that there is no evidence that “concern over player protests is having a material impact on our ratings.”

I have gone on record in multiple articles over the past few years talking about the likely impact of high profile or controversial events such as domestic violence incidents and concussions on NFL fandom.  My opinion has been that the NFL would continue to be strong and fans would continue to watch.  So what’s different now?  On this blog, the emphasis is almost always on data driven analyses.  In this case, it’s not possible to take that approach.  I would need much more detailed data on TV ratings and even then I likely wouldn’t have the ability to rule out different possible causes.

The NFL has suggested a confluence of events as the culprit.  I think this is true but perhaps not in the manner the NFL is implying.  I think the NFL is right that the presidential campaign is having an impact.  But I suspect it is having less of a direct impact due to people’s attention being shifted in a different direction.  College football does not seem to have experienced a decline in viewership.

I think it is the nature of the current political campaign and the emotions the campaign is generating.  This campaign has highlighted very distinct cultural differences.  The world views of Trump and Clinton supporters seem to be fundamentally different.  The potential problem is that a lot (majority?) of the NFL’s fan base may lean in the Trump direction while the protests lean in the Clinton direction.  In what follows I’m going to talk about this situation on a theoretical level.  I am making no value judgments about any protests or response to protests – I’m just looking at the marketing and branding issues.

Why are the protests potentially damaging to the NFL brand?  I think there are a couple of related issues.

First, the NFL has been known for shutting down individual expression by players.  Remember it is the No Fun League and it’s all about protecting the shield (brand).  Now, however, we seem to have a protest that is allowed.  And it is a protest with which many fans may disagree.  On some level the NFL seems to be changing its policies to accommodate the protests.  I think it is this “change” that may be the key issue.  Especially if the “change” is to accommodate something that is controversial to the core audience.  If a league is known for shutting down everything from TD celebrations to minor uniform violations then is not shutting something down an implicit endorsement?

The stridency of the current presidential campaign in terms of insiders versus outsider and political correctness makes this type of “authenticity” issue especially salient to certain segments of fans.  The impact may be  subtle.  It may manifest as a softening in enthusiasm or engagement with the NFL brand rather than a decrease in stated preference.  Fans still like the game and the players but maybe they are just not as compelled to watch.  (I don’t have access to the NFL’s data but this may be a tricky issue to assess using traditional marketing research techniques.)

The second, and related issue, is that there are other factors impacting the brand.  The current protests occurred in the wake of seasons that featured domestic abuse and the concussion issues.  The NFL brand may be resilient to any one event but over time problems can weaken the foundation.  This type of subtle brand weakness may be especially relevant given that the NFL is currently lacking some star power.  While the NFL is less of a star driven league than say the NBA, having Peyton Manning retired and Tom Brady suspended makes the league more vulnerable.

 

Questioning the Value of Analytics: Sports Analytics Series Part 3

Continuing the discussion about organizational issues and challenges, a fundamental issue is understanding and balancing the relative strengths and weaknesses of human decision makers and mathematical models.  This is an important discussion because before diving into specific questions related to predicting player performance it’s worthwhile to first think about how modeling and statistics should fit into an overall structure for decision making.  The short answer is that analytics should serve as a complement to human insight. 

The “value” of analytics in sports has been the topic of debate.  A high profile example of this occurred between Charles Barkley and Daryl Morey.  Barkley has gone on record questioning the value of analytics.

“Analytics don’t work at all. It’s just some crap that people who were really smart made up to try to get in the game because they had no talent. Because they had no talent to be able to play, so smart guys wanted to fit in, so they made up a term called analytics.  Analytics don’t work.” 

The quote reflects an extreme perspective and it is legitimate to question whether Charles Barkley has the background to assess the value of analytics (or maybe he does, who knows?).  But, I do think that Barkley’s opinion does have significant merit.

In much of the popular press surrounding books like Moneyball or The Extra 2% analytics often seem like a magic bullet.  The reality is that statistical models are better viewed as decision support aids.  Note that I am talking about the press rather than then books.

The fundamental issue is that models and statistics are incomplete.  They don’t tell the whole story.  A lot of analytics revolves around summarizing performance into statistics and then predicting how performance will evolve. Defining a player based on a single number is efficient but it can only capture a slice of the person’s strengths and weaknesses.  Predicting how human performance will evolve over time is a tenuous proposition.

What statistics and models are good at is quantifying objective relationships in the data.  For example, if we were interested in building a model of how quarterback performance translates from college to professional football we could estimate the mathematical relationship between touchdown passes at the college level and touchdown passes at the pro level.  A regression model would give us the numerical patterns in the data but such a model would likely have little predictive power since many other factors come in to play.

The question is whether the insights generated from analytics or the incremental forecasting power actually translate into something meaningful.  They can.  But the effects may be subtle and they may play out over years.  And remember we are not even considering the financial side of things.  If the best predictive models improve player evaluations by a couple of percent maybe it translates to your catcher having a 5% higher on base percentage or your quarterback having a passer rating that is 1 or 2 points higher.  These things matter.  But are they dwarfed by being able to throw 10 or 20 million more into signing a key player?

If the key to winning a championship is having a couple of superstars.  Then maybe analytics don’t matter much.  What matters is being able to manage the salary cap and attract the talent.  But maybe the goal is to make the playoffs in a resource or salary cap constrained environment.  Then maybe spending efficiently and generating a couple of extra is the objective.  In this case analytics can be a difference maker.