1. EVALUATING NHL DRAFT PROSPECTS A Historical Cohort Based Approach Cam Lawrence @moneypuck_ Josh Weissbock @joshweissbock puckprospectsconsulting@gmail.com

2. CHALLENGES IN PROSPECT EVALUATION  Limited or no data other than Goals, Assists, Points, PIM  Disparity in QoT/QoC across broad number of “feeder” leagues. For example: o SHL to NHL equivalency: 0.55 1 o WHL to NHL equivalency: 0.26 1 o Canadian Junior A to NHL equivalency: 0.14 2  Disparity in QoT/QoC within leagues. Example from 2014-15 OHL Season: o Sudbury Wolves: Goals/GM=2.19, Goals Against/GM=4.75 o Sault Ste. Marie Greyhounds: Goals/GM=5.09, Goals Against/GM=2.88 1 Per Rob Vollman via @robvollmanNHL 2 http://lowetide.ca/blog/2014/07/28/nhl-equivalencies/http://lowetide.ca/blog/2014/07/28/nhl-equivalencies/

3. WHAT WE’VE LEARNED  Height, Age (ie 17.2 vs 17.9 in draft year), and PPG are all statistically significant variables in CHL players for predicting NHL PPG (r-squared=23.3) 1,2. Note, question remains as to whether success rate of larger player is a factor of scouting bias towards larger players.  League quality is an important factor - 17 year-olds playing in the top European men’s leagues have a relatively high chance of making it in the SHL 3 1 http://canucksarmy.com/2015/2/10/draft-theory-height-matters-but-maybe-due-to-bias 2 http://thats-offside.blogspot.ca/2014/06/adjusting-scoring-rate-for-age-in-chl.html 3 http://canucksarmy.com/2015/1/28/the-nhl-draft-boys-playing-in-a-man-s-league

4. THE PROJECT – CAN WE USE HISTORICAL SUCCESS RATES OF COMPARABLE PLAYERS TO PREDICT FUTURE NHL SUCCESS?  With the help of Josh Weissbock (@joshweissbock) obtained data from all significant NHL feeder leagues from the period of 1988-89 to present  Grouped players from 1988-89 to 2003-04 into cohorts based on same or similar league, scoring rate, and height  Calculated % of NHL success rate, defined as >200 NHL GP 1, for each cohort  Applied predicted % of NHL success rate to players from 2004-05 to 2008-09 seasons and compared to actual % of NHL success 1 http://oilersnation.com/2015/1/23/development-and-the-200-game-markhttp://oilersnation.com/2015/1/23/development-and-the-200-game-mark

5. OBSERVED DATA: 17 YEAR OLD DRAFT ELIGIBLE SEASONS FROM 1988-89 TO 2003-04 LeagueAgePeriodNHLersTotal Players%correlation PPG/NHL PPG CHL171988-89 to 2003-04362 3,6709.86% 0.573 Jnr A (BCHL, AJHL, SJHL, MJHL, OJHL,…)171988-89 to 2003-0455 8176.73% 0.371 NCAA171988-89 to 2003-0417 10815.74% 0.544 Euro Elite Leagues (Slovak, Czech, SHL, Liiga, KHL)171988-89 to 2003-0473 33521.79% 0.392 Euro U20 Leagues171988-89 to 2003-0441 1,7232.38% 0.361 USHL & NAHL171988-89 to 2003-0446 6806.76% 0.359 594 7,3338.10% Exclusions:  Goalies  Overage Seasons – only 17 year old players were included in the analysis  Players with less than 10 games played were excluded  Players from the following leagues in their draft season: -Europe 2 nd division leagues (MHL, Allsvenskan, Czech 2) as these leagues had lower correlation with NHL success than either the U20 Junior leagues or the Elite leagues -USHS players – do to concerns over the completeness of USHS data available

6. HEIGHT DISTRIBUTION OF NHL PLAYERS (2013-14) 1-0 std 0-1 std +1< std >-1 std *Height distribution varies between forwards and defensemen 5’96’06’3

7. EXAMPLE OF COHORT HEAT MAP Note: Clear limitations exist both with the ‘edge cases’ (players close to the height/P/GM cut off) and some issues with small sample size for some of the high % cohorts. CHL ForwardsCHL Defensemen 5’96’06’2 5’10 6’1 6’3

8. PREDICTED DATA: 2005-2009 DRAFT ELIGIBLE 17 YEAR- OLDS  Predicted cohort % of success was applied to 3,697 draft eligible players for the 2005-09 NHL Drafts based on the 1988-89 to 2003-04 sample data:  Exclusions:  Overage Seasons – only 17 year old players were included in the analysis  Players with less than 10 games played were excluded  Players from the following leagues in their draft season: Europe 2 nd division leagues (MHL, Allsvenskan, Czech 2) as these leagues had lower correlation with NHL success than either the U20 Junior leagues or the Elite leagues USHS players – do to concerns over the completeness of USHS data available KHL players – do to apparent aversion of NHL teams to draft high % KHL prospects during this period Draft Year# DraftedPlayers in Sample 2005230664 2006213681 2007211745 2008211801 2009211806 10763697

9. PREDICTED DATA: 2005-2009 DRAFT ELIGIBLE 17 YEAR- OLDS  Predicted cohort % of success was applied to 3,697 draft eligible players for the 2005-09 NHL Drafts based on the 1988-89 to 2003-04 sample data:  Exclusions:  Overage Seasons – only 17 year old players were included in the analysis  Players with less than 10 games played were excluded  Players from the following leagues in their draft season: Europe 2 nd division leagues (MHL, Allsvenskan, Czech 2) as these leagues had lower correlation with NHL success than either the U20 Junior leagues or the Elite leagues USHS players – do to concerns over the completeness of USHS data available KHL players – do to apparent aversion of NHL teams to draft high % KHL prospects during this period Draft Year# DraftedPlayers in Sample 2005230664 2006213681 2007211745 2008211801 2009211806 10763697

10. CORRELATION RESULTS  High Correlation Between Predicted Cohort % Success and Actual Cohort % Success at both 100 game and 200 game thresholds:  High Correlation Between Predicted Cohort % Success and NHL P/GM: 0.497  High Correlation Between NHE equivalency adjusted P/GM and NHL P/GM: 0.486

11. SHAM 2.0 RESULTS  Test 1: Pick the highest % of success cohort player among the next 15 highest ranked per CSS. Higher GP than actual 5/12 times. Higher points 3/12 times.  Test 2: Pick highest NHLe adjusted P/GM player among next 15 highest ranked by % of success. Higher GP than actual 4/12 times. Higher points 10/12 times. Actual Draft ResultsTest 1: Next 15 CSS highest % successTest 2: Next 15 %success/NHLe TeamNHL GPNHL Pts P/GM * NHL GPNHL Pts P/GM NHL GPNHL Pts P/GM Anaheim 2,301 936 0.41 1,744 739 0.42 1,512 1,037 0.69 Boston 2,425 1,199 0.50 1,645 918 0.56 2,237 1,130 0.51 Buffalo 2,836 882 0.33 1,680 452 0.27 1,889 992 0.53 Carolina 1,197 606 0.51 2,166 653 0.30 1,494 888 0.59 Chicago 2,611 1,396 0.53 2,726 1,103 0.40 2,757 1,459 0.53 Columbus 4,408 1,380 0.34 1,709 562 0.33 2,629 1,272 0.48 Florida 1,595 462 0.30 2,171 573 0.26 1,924 1,016 0.53 New Jersey 1,971 544 0.28 1,200 293 0.24 1,517 865 0.57 Phoenix 2,959 1,335 0.45 2,164 995 0.46 2,581 1,451 0.56 Tampa Bay 1,837 876 0.48 2,601 1,416 0.54 2,622 1,552 0.59 Vancouver 1,311 560 0.43 1,603 499 0.31 1,581 967 0.61 Washington 3,125 1,343 0.53 2,321 1,018 0.44 2,882 1,597 0.55 28,576 11,519 0.42 23,730 9,221 0.39 25,625 14,226 0.56 * GP for Actual Goalies drafted removed from calculation of P/GM

12. LIMITATIONS OF CURRENT MODEL  Doesn’t indicate the quality of players who make up the cohort (ie replacement level or star?)  Issues with the edge cases – 0.749 PPG players in separate cohort than 0.751 players  While it may accurately predict a group of players may have a high % of success, you need to compliment with strong scouting to which will be the 60% who make it, versus the 40% that bust. An example of the top 3 ranked D by % success in 2007: NameDraftpositionheightLeagueGPPtsP/GM NHL GP % successDraft # CSS RankNotes ALEX PLANTE2007D196WHL588 0.661067%1572 212 AHL games. Currently in Norway JURAJ VALACH2007D202WHL587 0.52067%undrafted125309 GP in Cz/Slov. Elite Leagues P.K. SUBBAN2007D183OHL6815 0.8234453%43102He's pretty good…

13. FUTURE WORK  Use of advanced mathematical methods to mitigate issues around ‘edge cases’  Database project to produce list of the players that make up peer group to assist in qualitative analysis  Addition of other factors (draft year age, NHLe P/GM, QoT metrics) to improve overall precision

14. FUTURE WORK  Use of advanced mathematical methods to mitigate issues around ‘edge cases’  Database project to produce list of the players that make up peer group to assist in qualitative analysis  Addition of other factors (draft year age, NHLe P/GM, QoT metrics) to improve overall precision