Why is sports analytics valuable?
Because humans have mental & situational constraints.
A thread 👇
Because humans have mental & situational constraints.
A thread 👇
1/ Sports analytics has been able to provide value beyond traditional qualitative judgments because humans have limits that can be generally classified as:
- Mental constraints
- Situational constraints
- Mental constraints
- Situational constraints
2/ Let's start with the mental constraints.
Perhaps the most obvious mental constraint is that our memory is limited.
We don't have the capacity to remember and process everything that happened on every play.
Perhaps the most obvious mental constraint is that our memory is limited.
We don't have the capacity to remember and process everything that happened on every play.
3/ Our minds are also subject to cognitive biases.
These biases distort our ability to accurately process information.
A number of cognitive biases have been discovered over the years, but one of the most well-known is "availability bias"
These biases distort our ability to accurately process information.
A number of cognitive biases have been discovered over the years, but one of the most well-known is "availability bias"
4/ Availability bias (or availability heuristic) is the tendency of humans to overestimate the frequency of particularly memorable events.
5/ A commonly-used illustrative example is that of a person who's afraid to fly for fear that plane crashes are common.
In reality, plane crashes are very rare, but when they do happen, they're particularly memorable, so our mind overestimates how often they happen.
In reality, plane crashes are very rare, but when they do happen, they're particularly memorable, so our mind overestimates how often they happen.
6/ How could this apply to sports?
When an athlete makes a particularly amazing or terrible play, we’re likely to remember that play.
We're less likely to remember the times that the same player did something relatively common because common performances aren't as memorable.
When an athlete makes a particularly amazing or terrible play, we’re likely to remember that play.
We're less likely to remember the times that the same player did something relatively common because common performances aren't as memorable.
7/ And because of availability bias, we might overestimate the frequency with which a player makes amazing or terrible plays, distorting our overall perception of the player.
8/ Data helps us keep availability bias in check because, when analyzed correctly, data remembers everything equally and doesn’t focus the memorable moments in the way that humans tend to.
9/ For more on cognitive biases, check out the work of Kahneman & Tversky, who are perhaps the most important figures in the history of the subject.
Kahneman's "Thinking Fast and Slow" is a good place to start:
amazon.com/Thinking-Fast-…
Kahneman's "Thinking Fast and Slow" is a good place to start:
amazon.com/Thinking-Fast-…
10/ Moving on to situational constraints...
Situational constraints typically boil down to limits on time & resources.
Team decision-makers don't always have time to thoroughly evaluate all players or the resources to hire more scouts to do it for them.
Situational constraints typically boil down to limits on time & resources.
Team decision-makers don't always have time to thoroughly evaluate all players or the resources to hire more scouts to do it for them.
11/ There’s only so much time in a day.
If you’re trying to evaluate prospective players, you may not have the time to digest the quantity of game film necessary to understand the full picture of each prospect’s career.
If you’re trying to evaluate prospective players, you may not have the time to digest the quantity of game film necessary to understand the full picture of each prospect’s career.
12/ Obviously, teams tend to deal with this issue by hiring scouts.
If you’re the GM of a team with significant resources, you can afford to hire a team of talent evaluators to spread around the work of watching and evaluating film.
If you’re the GM of a team with significant resources, you can afford to hire a team of talent evaluators to spread around the work of watching and evaluating film.
13/ But what if you're the GM of a team without the resources to hire a massive team of talent evaluators or the pool of prospective talent is just too vast?
14/ This is where data can come into play.
Data analysis can be done in aggregate on many players at once, allowing teams to analyze thousands of prospects simultaneously.
Data analysis can be done in aggregate on many players at once, allowing teams to analyze thousands of prospects simultaneously.
15/ How would this work in practice?
Let's say a ⚽ team is looking for a left back that fits a particular playing-style profile.
There could be 1000s of left backs in the world, and the team's scouts don't have time to thoroughly evaluate all of them.
Let's say a ⚽ team is looking for a left back that fits a particular playing-style profile.
There could be 1000s of left backs in the world, and the team's scouts don't have time to thoroughly evaluate all of them.
16/ The analytics staff could build a model that identifies the top players in the world that fit the left back profile the team is looking for.
With this model, the team could filter the initial pool of 1000s of left backs down to the top 10 prospects.
With this model, the team could filter the initial pool of 1000s of left backs down to the top 10 prospects.
17/ 10 prospects is much more manageable than 1000s for the scouts, who could jump into the process at this point and begin qualitative analysis on this smaller group.
18/ In summary...
Sports analytics can add value because humans have limits that can be generally categorized as:
- Mental constraints (memory limits & cognitive biases)
- Situational constraints (limited time & resources)
Sports analytics can add value because humans have limits that can be generally categorized as:
- Mental constraints (memory limits & cognitive biases)
- Situational constraints (limited time & resources)
19/ For a slightly deeper dive on this subject, check out my blog post "The Case for Sports Analytics":
brendankent.com/2020/10/08/spo…
brendankent.com/2020/10/08/spo…
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