EVALUATING NHL DRAFT PROSPECTS A Historical Cohort Based Approach Cam Josh

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: o WHL to NHL equivalency: o Canadian Junior A to NHL equivalency:  Disparity in QoT/QoC within leagues. Example from 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= Per Rob Vollman 2

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

THE PROJECT – CAN WE USE HISTORICAL SUCCESS RATES OF COMPARABLE PLAYERS TO PREDICT FUTURE NHL SUCCESS?  With the help of Josh Weissbock obtained data from all significant NHL feeder leagues from the period of to present  Grouped players from to 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 to seasons and compared to actual % of NHL success 1

OBSERVED DATA: 17 YEAR OLD DRAFT ELIGIBLE SEASONS FROM TO LeagueAgePeriodNHLersTotal Players%correlation PPG/NHL PPG CHL to , % Jnr A (BCHL, AJHL, SJHL, MJHL, OJHL,…) to % NCAA to % Euro Elite Leagues (Slovak, Czech, SHL, Liiga, KHL) to % Euro U20 Leagues to , % USHL & NAHL to % , % 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

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

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

PREDICTED DATA: DRAFT ELIGIBLE 17 YEAR- OLDS  Predicted cohort % of success was applied to 3,697 draft eligible players for the NHL Drafts based on the to 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

PREDICTED DATA: DRAFT ELIGIBLE 17 YEAR- OLDS  Predicted cohort % of success was applied to 3,697 draft eligible players for the NHL Drafts based on the to 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

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:  High Correlation Between NHE equivalency adjusted P/GM and NHL P/GM: 0.486

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, , ,512 1, Boston 2,425 1, , ,237 1, Buffalo 2, , , Carolina 1, , , Chicago 2,611 1, ,726 1, ,757 1, Columbus 4,408 1, , ,629 1, Florida 1, , ,924 1, New Jersey 1, , , Phoenix 2,959 1, , ,581 1, Tampa Bay 1, ,601 1, ,622 1, Vancouver 1, , , Washington 3,125 1, ,321 1, ,882 1, ,576 11, ,730 9, ,625 14, * GP for Actual Goalies drafted removed from calculation of P/GM

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 – PPG players in separate cohort than 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 PLANTE2007D196WHL % AHL games. Currently in Norway JURAJ VALACH2007D202WHL %undrafted GP in Cz/Slov. Elite Leagues P.K. SUBBAN2007D183OHL %43102He's pretty good…

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

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