## Games Playoffs Power Ranks Goalies About |

The data used to predict each game includes each team's performance of a range of statistics in the season up until the date of the given game. Older games are given less weighting for the statistic. The weighting each game is given is linear. For example, when predicting the result of a team's 41st game of the season, the team's 40th game is given twice the weight as a team's 20th game. Several game weighting techniques were tested as part of building the model, from weighting each game equally to weighting recent games exponentially more. Also, using just the last 20 or 30 games data was evaluated in combination with each one of these methods. Ultimately, using full season to date data with a linear decay of game importance showed to have the most predictive power.

There are three main components of the win predictions model: the Home submodel, the Away submodel, and the Meta model:

The Home submodel uses only statistics describing the home team and predicts the likelihood the home team will win the game.

The Away submodel similarly uses only statistics describing the away team and predicts the likelihood the away team will win the game.

The Meta Model combines the Home submodel, Away model, home ice advantage, and each team's days of rest into one overall score.

Also, we use a simple 'Tie Game' model, which predicts the probability the game will go to overtime. This model is simply a function of the meta model score. The default Tie Game model score is 25%. For every 1% probability the Meta Model is away from a 50/50 odds game, the chance of the game going to OT goes down by 0.2%. For example, a game with 55/45 odds is given a 24% chance of going to OT. If a game goes to OT, we give each team equal odds of winning the game, whether in regular OT or a shootout.

The home team's overall odds of winning the game are then calculated as follows:

Home Team chances of winning In regulation = (1 - [Tie Game Model Score]) * [Meta Model Score]

Home Team chances of winning in OT = [Tie Game Model Score] * 50%

Home Team chances of winning = Chance of winning in regulation + chance of winning in OT

By using the 2015-2016 season as a test to see if the model works, the 15% of shots the model rated the highest contributed to over 50% of the goals that season:

In general, the shots with the highest goal probability are quick rebounds shots close to the net where there has been a large change in shot angle from the original shot:

1.) Shot Distance From Net

2.) Time Since Last Game Event

3.) Shot Type (Slap, Wrist, Backhand, etc)

4.) Speed From Previous Event

5.) Shot Angle

6.) East-West Location on Ice of Last Event Before the Shot

7.) If Rebound, difference in shot angle divided by time since last shot

8.) Last Event That Happened Before the Shot (Faceoff, Hit, etc)

9.) Other team's # of skaters on ice

10.) East-West Location on Ice of Shot

11.) Man Advantage Situation

12.) Time since current Powerplay started

13.) Distance From Previous Event

14.) North-South Location on Ice of Shot

15.) Shooting on Empty Net

By leveraging the season simulations in the event of each of a regulation win, regulation loss, OT loss or OT win, we can see the impact of playoff odds in real time as the odds of different outcomes of the game change.

Below is a graph of the % of time the team that were considered the 'favorites' ended up winning the game. Before games start we can predict ~57% correctly. As games continue our confidence gradually goes up until ~89% at the end of regulation. Of the games that go to OT, the outcome is basically a coinflip.

The MoneyPuck logo was kindly created by Vivian Chan.

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