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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Calculating the value of a draft pick is not an easy task – there’s a great amount of uncertainty involved in any given pick, and the number of “misses” are significantly more than the number of “hits”, particularly in later rounds. Compounding the issue is the lack of a clear definition for success – while most methods to date have defined a successful pick as a player who reached the 200 NHL game mark, this approach is flawed in that it ignores differences in contribution and is biased against higher round picks. In this article, we’ll look at how to address these issues with a few simple changes to the standard approach, and use a novel evaluation approach to build a model that should more accurately estimate the value of an NHL draft pick.
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Tagged with: Draft
Posted in Draft
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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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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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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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.
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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