Historical Player Projections – Available Now at Dobber Hockey

Posted on September 23, 2015 by Matt Cane — Leave a comment

Over the last few years, Hockey Analytics pioneer Rob Vollman has been putting out a book of scoring projections using historically comparable players to calculate a player’s best case/worst case/average scoring totals. His method is really interesting and yields a lot of great fodder for discussion (for example, is David Clarkson really a similar player to Donald Brashear?), as well as a useful baseline to build a fantasy team off of.

This year, he was kind enough to invite me to contribute to the project, as I put together a similar set of comparable players and projections using some of the modern “enhanced” stats that we now have available. The full guide, with complete projections on over 700 NHL players, is available for $4.99 in the Dobber Store here, or is also available for free (free!!!) with your purchase of the complete 2015-2016 Dobber Fantasy Hockey Guide.

As a sneak peak of what’s inside, we’re going looking at one player from each team and analyzing what his comparables suggest is in store for the year to come. The whole series will be up on Dobber Hockey and will be going up over the next few weeks. To date, we’ve got articles up on:

I’ll keep this list updated as we go through the series, so you can always check back here to see what’s new.

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Posted in Fantasy Hockey, Predictions

Examining The Drivers of Score Effects I: Shooting and Save Percentage

Posted on August 10, 2015 by Matt Cane — 6 Comments

Score effects – they’re real, and they’re spectacular. And while the idea and impact of score effects are generally understood by most in the hockey analytics community, they can often be a difficult subject to introduce to newcomers to the field. A solid knowledge of score effects is critical to understanding why teams that outshoot their opponents in a given game tend to lose more often than not, or why a player or team’s unadjusted statistics may be misleading you.

Furthermore, while many people have hypothesized about what causes score effects, there’s currently little documented analysis examining the actual drivers of score effects. So consider this article, and its coming sequel, as first steps at drilling deeper into the factors that lead us to observe score effects. Hopefully these pieces will serve as both a reference point to provide a basic understanding of score effects, and as evidence that helps justify any adjustments we do make when devising new statistics.

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Posted in Score Effects

Predicting Free Agent Salaries

Posted on June 28, 2015 by Matt Cane — 9 Comments

With the NHL draft behind us, attention across the hockey world has turned to July 1st, when unrestricted free agents will be free to sign with the team that will give them the best shot at winning a cup the highest bidder, and fans of all franchises pray that their GM doesn’t screw anything up for a superstar to vault them into contender status. And while every deal signed will be scrutinized from a million different angles to determine whether or not a team paid fair money, most of this discussion will end up conflating the ideas of value (will Martin St. Louis still be putting up 20+ goals at age 42?) and price (should the Bruins, err Flames, really pay Dougie Hamilton $5.5MM per year?).

This, unfortunately, is a rather large mistake, because what teams pay for a player and what that player is actually worth to a team are two critical yet extremely different questions. Worth (or value) is what a player adds to a team on the ice (in goals or wins), and must be measured based on a player’s total contribution (see, for example, WAR on Ice’s Wins Above Replacement, Hockey Reference’s Point Shares, or Hockey Prospectus’ Goals Versus Threshold). Price, on the other hand, is simply what a team is willing to pay for a player, in contract dollars or cap hit. While ideally these numbers would align, the reality is that teams often value observed historical results more than they should. Teams tend to pay for basic counting stats while ignoring other potentially useful indicators of future success (such as general shot generation or prevention) or contextual factors (such as quality of teammates).

This market inefficiency presents an opportunity for teams, as GMs that can identify which players are over or underpaid relative to their actual contribution should have a long-term advantage in a cap-restricted world. The key to taking advantage of these opportunities, however, is being able to predict what your opponents are going to do. If a general manager knows what the rest of the league will pay a player, this information can be used to help identify potential targets in free agency before the negotiating period begins. Given that marquee free agents can often sign within hours of hitting the open market, this knowledge can help teams avoid chasing after players they know will be out of their budget. On the other hand, it can also help GMs know when they can play hard ball with their own players and refuse to submit to offers that are above what the market is likely to pay. All of which is to say that there’s a lot of value in being able to guess what the other 29 clubs in the league will be willing to pay a free agent, which brings us to the main question we’ll look at in this article: can we build a model to predict how much a free-agent will end up signing for, based on his historical stats?

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Posted in Free Agency, Predictions

Puck++ Playoff Prediction Challenge – Round 2 Results

Posted on May 14, 2015 by Matt Cane — Leave a comment

With round 2 of the playoffs wrapped up and a few days of rest penciled into the schedule, there’s no better time to check in on how the entrants in the Puck++ Playoff Prediction Challenge are doing. As a reminder on the format, each entrant provided the probability that each team would win a given series, with each entry scored using the Brier Score. Entrants who provided picks for round 1 but missed the round 2 deadline were given a default guess of 0.5 in each series to keep things interesting and to allow people to jump back in for round 3. I’ve also included a Naïve set of predictions, which were generated using the regular season results as a measure of a team’s “true talent”.

With all that said, let’s take a look at who’s sitting in the driver’s seat heading into the conference finals:

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Posted in Predictions

Puck++ Playoff Prediction Challenge – Round 1 Results

Posted on May 3, 2015 by Matt Cane — Leave a comment

With round 1 wrapped up and the division finals now underway, it’s time to take a look at how the entrants in the first ever Puck++ Playoff Prediction Challenge performed. The table below has each entrants total score (using the Brier Score, lower is better) as well as the number of series that each entrant correctly predicted.

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Posted in Playoffs

2015 Playoff Predictions

Posted on April 15, 2015 by Matt Cane — Leave a comment

After three seemingly never ending nights without hockey, the playoffs are finally upon us, with the Habs and Sens, and Caps and Isles set to kick off the postseason tonight at 7. As in years past I’m going to be posting predictions for each matchup as well as overall cup odds throughout the playoffs. Unlike the last 2 years, however, I’m using a new (and hopefully improved) model to predict how well each team will do.

This new model is based partly on work that I’ve completed and written about in the past (for example, a major element is Score Adjusted Weighted Shots), and partly on work that I’ve done but haven’t written about. I’ll hopefully get into more detail on the model later, but at a high level the key factors are:

  • 5v5 Score Adjusted Weighted Shots For Per 60 (over the last 25 games)
  • 5v5 Score Adjusted Weighted Shots Against Per 60 (over the last 25 games)
  • Penalties Drawn Per 60 (full season)
  • Penalties Taken Per 60 (full season)
  • Powerplay Weighted Shot Differential Per 60 (full season)
  • Penalty Kill Weighted Shot Differential Per 60 (full season)

Both the Powerplay and Penalty Kill shot weights are different from the even strength weights, but the principles used in deriving the weights are the same. All these factors are scaled against league average and then used to determine each team’s advantage on offense, defense and special teams. Based on these advantages we can predict each team’s likelihood of defeating their opponent in a single game, and extrapolate these single game numbers to find a total series probability.

So who does our new model think will be accepting the Cup from Gary Bettman this spring?

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Posted in Predictions

Puck++ Playoff Prediction Challenge

Posted on April 12, 2015 by Matt Cane — Leave a comment

Do you like the playoffs? Do you like predicting things? And do you think the NHL’s claim that SAP has a guy who can get 85% of playoff series right is a bit unbelievable? Then do we here at Puck++ have a contest for you!

For the first (and hopefully not last) time, Puck++ is hosting a Playoff Prediction Challenge. To enter, all you need to do is provide the probability of the home team winning each series, round by round. The entry form and a full set of rules are available here – entries will be accepted at any time, however you’ll only be able to provide predictions for any series that has not yet begun, so don’t wait until the last minute to get your picks in.

What do the winners get? Glory, and the right to call yourself the smartest person in the hockey world for the next year*.

So what are you waiting for? Entering takes as little or as much time as you’d like, so fire up R or Python or your crystal ball and get predicting!

*Puck++ provides no guarantee that you will be recognized as the smartest person in the hockey world.

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Posted in Playoffs

Predicting Save Percentage: Dangers Zones and Shot Volumes

Posted on March 23, 2015 by Matt Cane — 14 Comments

A few days ago, Conor Tompkins of Null Hypothesis Hockey tweeted out an interesting set of graphs showing the correlation between a goalie’s save percentage in each of the War-On-Ice danger zones and their overall success rate. Conor found that (unsurprisingly) a goalie’s performance on high danger shots was most closely correlated with overall success, with medium shots having slightly less influence, and low danger shots showing almost no relationship. While Conor’s model focused on correlations within the same season, Sam Ventura suggested that a useful extension would be to look at how well the danger zone save percentages predicted future overall save percentages. After all, if performance on high danger shots is most critical for a goalie in determining his current season save percentage, it stands to reason that this would also be a key predictor of future success.

One way we can look at this is to run a multiple linear regression between a goalie’s current season save percentage and his past save percentages broken down by danger zone. We’ll focus on 5v5 data only to avoid the issue of varying penalty rates between teams, and look at goalies who played at least 1000 minutes in back-to-back seasons (all data from War On Ice).

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Posted in Goaltending, Theoretical

Calculating Replacement Level for Faceoff Win Percentage

Posted on March 8, 2015 by Matt Cane — Leave a comment

The idea of the replacement level player is one of the most important concepts in sports analytics. While not strictly necessary to do any basic player comparisons, the value of the replacement level lies in providing a baseline below which a professional player should not perform. After all, if a player is performing below replacement level, we should listen to the stats and do exactly what they tell us to: replace him with almost any other player.

In hockey though defining replacement level can be a difficult task. Part of that stems from the fact that we currently don’t have exact methods of rating player’s individual contributions. We can say which players generally perform well when they’re on the ice, and we can estimate how a player’s team performs with and without him, but distilling all the information we have down to an opinion about a player’s value is currently more art than science. Hockey is a complex game with many moving parts and because of that creating an aggregation method for all the data we gather is a complex task. Read more ›

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Posted in Faceoffs

Hockey Prospectus – Player Level Weighted Shots

Posted on February 26, 2015 by Matt Cane — 1 Comment

Over at Hockey Prospectus I’ve got an article up on calculating Weighted Shots (or, more specifically, Score Adjusted Weighted Shots) at the individual player level. Give it a read, here. The article expands on my presentation at the Ottawa Hockey Analytics Conference, which you can find here.

Lastly, if you’re interested in seeing the player level SAwSH data from 2008-2009 through to 2013-2014, it’s available here.

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Posted in Hockey Prospectus