1. Controlling for Context S. Burtch © 2014

2. Traditional Points Plus/Minus Faceoffs Real Time Stats (hits, blocked shots, takeaways/giveaways) Ice Time

3. Rate Statistics Possession Metrics (On-Ice) Shots For / Against Shot Attempts For / Against(aka Corsi) Unblocked Shot Attempts For / Against (aka Fenwick) Shot Attempt Differentials Shot Attempt Percentages

4. Not all minutes/situations are created equal – we are aware of Contextual impacts resulting from usage. We’re working within a dynamic system – teasing out impacts of individuals will never be a simplistic process.

5. Zone Starts Time on Ice Quality of Teammates Quality of Competition (more on this later) Face Off Wins/Losses Aging Score Effects Time Effects (in game) Shot Type / Location

6. Eyeballing all the various components REL Measures (On Ice - Off Ice differential) - Desjardins WOWY Charts – Tango (via Johnson) Usage Charts – Vollman Aging Trajectories / DELTA – Tango Heavy Lifter Index - Poplichak Score Adjusted Metrics – Tulsky Zone Start Adjusted Metrics – Tulsky Hextally – Thomas / Ventura

7. THoR – Schuckers Expected Goals – Parkatti/Pfeffer Delta SOT – Awad Goals Above Baseline – Thomas / Ventura delta Corsi – Burtch

8. Need to assess defenders better Defenders don’t seem to have a massive amount of impact on SV% or SH% Defenders DO seem to repeatably impact on Shot Attempt Differentials Initially patterned after HLI (Poplichak) – but specific to D men Unhappy with index values / basis for comparison / weighting Shifted to a regression model to predict outcomes Multivariate Linear Regression Model to predict Expected Corsi

9. Corsi For and Corsi Against are very weakly correlated – Offense does NOT equate to Defense at the team or individual skater level. This means we should regress CF and CA separately Position is Relevant (F and D impact CF/CA differently) QoT effects are very significant QoC effects are marginal to nonexistent This aligns with the work done by Tulsky and Johnson TOI is a significant correlate to results (better players tend to play more) Zone Starts are significant Faceoffs matter – but less than people think

10. Yearly Team Effects are very significant This is likely an indication of team quality / systems / coaching Age effects are hard to detect but appear to be present CONTEXT MATTERS VERY MUCH – It Explains Over 64% of observed results for Corsi.

12. dCorsi represents the residual (differential) between a player’s Observed Corsi and their Expected Corsi resulting from the discussed regression. This is an improvement on Corsi REL because it is determined directly from contextual factors while the player is ON the ice (the OFF Ice results aren’t weighted equivalently to the other factors).

13. 1 SD of dCorsi for NHL skaters is ± 2.0404, population mean µ = 0.019 dCorsi is repeatable at a higher level than individual skater SH% or goaltender SV%. Year to Year 5v5 SV% for goalies with 500+ mins has an autocorrelation coefficient of r = 0.0646, which translates to an r 2 of 0.004171 – the prior year explains 0.4% of the following year’s result. The r 2 year over year for Expected Corsi exceeds 43%, and for dCorsi is approximately 15%. dCorsi accounts for yearly team effects so players are not impacted negatively/positively by transitioning from one team to another year to year. Impacts of Coaching Effects Can Plausibly Be Observed Year to Year.

14. Tableau Visualizations have been created for tracking individual skaters and to make team comparisons. http://public.tableausoftware.com/shared/5MKNXWTX4?:display_count =yes http://public.tableausoftware.com/shared/G6HYCG29P?:display_count= yes THANKS!