Machine Learning and Hockey: Is there a theoretical limit on predictions?

Updated: August 1, 2013 at 9:39 am by Josh W

Graph from Arctic Ice Hockey 


In my previous work I was working on predictions in hockey, at the micro level of a single game, to determine who will win and who will lose. I was using Machine Learning techniques (or algorithmic modeling) and one thing I’ve noticed from my work, and from reading papers on other peoples material, is that for every sport there seems to be a prediction limit (that is different for every sport). This is what I want to explore; how much of the results that we see are based on luck, and is there a theoretical prediction limit in the NHL? 

In this post I will be focusing on the first question, what part of the standings is made up by luck?


This subject is not brand new, it’s been touched on a few times on the internet and sports analytics community but I have not yet seen it in academic sources. I need to do the math myself in order to bring it to the academic community, show it is possible to calculate, rather than just assume the numbers on websites are correct. 

The oldest source of information I could find goes way back to 2006 in the comments on this sports blog. On a similar topic of luck in the standings there is this post looking at basketball. Arctic Ice Hockey had a post a few years back but just stated that luck is 38% of the results, but I wasn’t happy with trying to recreate it from the lack of detail. I also found this post which looks at calculating the theoretical limit in the NFL.

What is luck

We talk about luck a lot in sports analytics, we even have the stat PDO to show how lucky a person or a team has been. But a lot of people tend to dismiss it saying there’s no such thing as luck.  To start out, we need to define what luck exactly is. In the statistics community they refer to luck as a stochastic process where regardless what the initial conditions are there are many different processes that can evolve. 

Bringing this to hockey, over a season the luck of a team regresses to the norm over 82 games.  But in a single game luck plays an important part. Given how few events (goals) there are in a game, a single can be won by a puck deflecting off of a player. If that player was standing in a slightly different location or facing a different direction then the puck may not have gone in. It’s easy to start to see how luck can be important in a single game. Patrick D expanded upon this to show how luck plays more of a role especially in the shortened season we just had. 


To calculate luck in the standings we can compare the standard distributions (St.Dev) between a binomial distribution and the observed distribution; this is what I believe that Arctic Ice Hockey did.  We can also use Classical Test Theory.  In this we get the formula that

variance(observed) = variance(talent) + variance(error)

 I collected all the win percentages (win%) of all teams between 2004-205 and 2011-2012.  I excluded 2013 season because of the lockout and I treated all OT losses as a loss and OT wins as a win. There’s only two outcomes, win or loss. This gives a St.Dev of all win% over this time period of 0.09. The variance of this error can be calculated as (.5*.5)/82.

Plugging these numbers into the formula we get:

var(obs) = var(talent) + var(error)
var(talent) = var(obs) – var(error)
var(talent) = 0.09^2 – (.25/82)
var(talent) = 0.0081 – 0.003049
var(talent) = 0.005051

Now this is our estimated variance due to talent.  We can then find out what portion of the observed results is made up by talent. So:

luck = 1 – (0.005051 / (0.09^2)) = 0.376419753

We can say that luck explains 37.64% of the variance in the results in the standings.

Future Work and Conclusion

This number seems to be in line with the numbers presented by Arctic Ice Hockey. Next, I will be expanding on this to see if there is a theoretical limit to predicting in hockey. Some would argue that this limit is just 100%-37.64%, or ~62%.  I will need to do the math myself before I can agree with that stement.

Thanks and credit

There’s many people I’ve been in discussion with this while trying to figure it out, so many thanks to those.  This include Michael Guerault, Phil Birnbaum, Patrick D, Gabriel Desjardins and many more. 

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