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HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall.FORECHECKING DATA
October 22, 2019. Daniel Weinberger Data Analysis, Forechecking Data, Passing Data Leave a comment. Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xGdefinitions).
PASSING DATA
Visualizing passes isn’t easy in hockey. In any given KHL game, there are between 700 and 900 Passes. Somewhere between 65% to 85% are successful*. If you wanted to focus on just the successful ones, you’d have to find a way to meaningfully and concisely represent500-700 events.
FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the THE NHL PLAYOFFS AND PENALTIES In the regular season (for 5v5), these teams were called for 3.16 penalties per game and 4.09 penalties per sixty minutes. In the playoffs, the teams were called for 3.31 penalties per game and 4.16 penalties per sixty minutes (again, all for 5v5). I do believe that referees let more go in the playoffs. BEHIND THE NUMBERS: WHAT MAKES A STAT GOOD The first and simplest way we analyze a statistic is in sample correlations: how two numbers relate in the same sample. Generally speaking, the two numbers we compare are the statistic in question and goals, since outscoring is the ultimate objective to hockey (no, really). We look at how these two stats compare within the same set ofgames.
EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 Exceeding Pythagorean Expectations: Part 1. “ Nashville Predators vs Detroit Red Wings, 18. April 2006 ” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season. This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You canNHL SHIFT LENGTH
Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later. Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98,so I
A NEW LOOK AT AGING CURVES FOR NHL SKATERS (PART 1 Here is the cumulative difference represented in graphical form. I’ve used the “Year II” age for the age label (the average change from 22-23 is labeled as 23, for instance): Initially, it looks like the average NHL skater “peaks” at 23. However, since the change between ages 22 and 25 is minimal, it might be better to saythe average
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall.PASSING DATA
Visualizing passes isn’t easy in hockey. In any given KHL game, there are between 700 and 900 Passes. Somewhere between 65% to 85% are successful*. If you wanted to focus on just the successful ones, you’d have to find a way to meaningfully and concisely represent500-700 events.
PREDICTIVE MODELLING The average on-ice shooting and save percentages a player experiences tends to be influenced by their average time on ice per game. This relationship likely occurs due to a combination of factors: shooting talents of linemates and opponent, defensive talents of linemates and opponent, system and psychological effects, and an effect I like to call “streak effects”. FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the THE NHL PLAYOFFS AND PENALTIES In the regular season (for 5v5), these teams were called for 3.16 penalties per game and 4.09 penalties per sixty minutes. In the playoffs, the teams were called for 3.31 penalties per game and 4.16 penalties per sixty minutes (again, all for 5v5). I do believe that referees let more go in the playoffs. HOW MUCH DO NHL PLAYERS REALLY MAKE? PART 2: TAXES Canadian provinces all tax in tiers like the federal government does, with certain rates being owed on varying segments of salary. All NHLers fall into these provinces’ highest level. Quebec ’s top tier has the highest rate of provinces with an NHL team, as all earnings over $104,765 are taxed at 25.75 percent.. So, a player on the Maple Leafs or Senators would first owe the federal rateNHL SHIFT LENGTH
Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later. Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98,so I
A NEW LOOK AT AGING CURVES FOR NHL SKATERS (PART 1 Here is the cumulative difference represented in graphical form. I’ve used the “Year II” age for the age label (the average change from 22-23 is labeled as 23, for instance): Initially, it looks like the average NHL skater “peaks” at 23. However, since the change between ages 22 and 25 is minimal, it might be better to saythe average
EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 Exceeding Pythagorean Expectations: Part 1. “ Nashville Predators vs Detroit Red Wings, 18. April 2006 ” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season. This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You canHOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall. THE STATE OF GOALIE PULLING IN THE NHL IDENTIFYING PLAYING STYLES WITH CLUSTERING FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK NHL Player Size From 1917-18 to 2014-15: A Brief Look. February 19, 2015. March 5, 2015. Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: Aging, Aging Curve, NHL History. Image by Erich Schutt, via Wikimedia Commons. As any person interested in hockey stats should do, I’ve been gradually building my own personaldatabase
EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they take BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTICSEE MORE ONHOCKEY-GRAPHS.COM
NHL FORWARDS VS. DEFENSEMEN HEIGHT & WEIGHT, 1917-18 TO NHL Forwards vs. Defensemen Height & Weight, 1917-18 to 2014-15. Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see ifthere
HOCKEY GRAPHS
Visualizing and analyzing hockey and statistics. The recently agreed CBA extension and MOU (April 2020) includes provisions suggesting a flat salary cap for years to come, and as a result, general managers and players have experienced an unprecedented draft, free agency and arbitration marketplace this fall.PASSING DATA
Visualizing passes isn’t easy in hockey. In any given KHL game, there are between 700 and 900 Passes. Somewhere between 65% to 85% are successful*. If you wanted to focus on just the successful ones, you’d have to find a way to meaningfully and concisely represent500-700 events.
PREDICTIVE MODELLING The average on-ice shooting and save percentages a player experiences tends to be influenced by their average time on ice per game. This relationship likely occurs due to a combination of factors: shooting talents of linemates and opponent, defensive talents of linemates and opponent, system and psychological effects, and an effect I like to call “streak effects”. FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the THE NHL PLAYOFFS AND PENALTIES In the regular season (for 5v5), these teams were called for 3.16 penalties per game and 4.09 penalties per sixty minutes. In the playoffs, the teams were called for 3.31 penalties per game and 4.16 penalties per sixty minutes (again, all for 5v5). I do believe that referees let more go in the playoffs. HOW MUCH DO NHL PLAYERS REALLY MAKE? PART 2: TAXES Canadian provinces all tax in tiers like the federal government does, with certain rates being owed on varying segments of salary. All NHLers fall into these provinces’ highest level. Quebec ’s top tier has the highest rate of provinces with an NHL team, as all earnings over $104,765 are taxed at 25.75 percent.. So, a player on the Maple Leafs or Senators would first owe the federal rateNHL SHIFT LENGTH
Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later. Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98,so I
A NEW LOOK AT AGING CURVES FOR NHL SKATERS (PART 1 Here is the cumulative difference represented in graphical form. I’ve used the “Year II” age for the age label (the average change from 22-23 is labeled as 23, for instance): Initially, it looks like the average NHL skater “peaks” at 23. However, since the change between ages 22 and 25 is minimal, it might be better to saythe average
EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 Exceeding Pythagorean Expectations: Part 1. “ Nashville Predators vs Detroit Red Wings, 18. April 2006 ” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season. This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You canFORECHECKING DATA
October 22, 2019. Daniel Weinberger Data Analysis, Forechecking Data, Passing Data Leave a comment. Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xGdefinitions).
PREDICTIVE MODELLING Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. IDENTIFYING PLAYING STYLES WITH CLUSTERING BEHIND THE NUMBERS: WHAT MAKES A STAT GOOD The first and simplest way we analyze a statistic is in sample correlations: how two numbers relate in the same sample. Generally speaking, the two numbers we compare are the statistic in question and goals, since outscoring is the ultimate objective to hockey (no, really). We look at how these two stats compare within the same set ofgames.
ZONE TIME | HOCKEY GRAPHS As some of you know, the NHL tracked offensive zone time for two seasons, 2000-01 and 2001-02, then inexplicably stopped. As some of you also know, I have a lot of historical game data, and that includes all the zone time from these seasons. Taking those performances, and focusing on the first two periods to avoid any major score effects (or NHL COACHING CHANGES Michel Therrien has an interesting distinction in the research I’ve been doing about NHL coaching changes: he’s given me 4 instances where he and his replacement have coached 20+ games within the same season.He’s also replaced or been replaced in three of those instances by legit coaching talent – he replaced Alain Vigneault for the Montreal Canadiens in 2000-01, was replaced two years THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 Exceeding Pythagorean Expectations: Part 1. “ Nashville Predators vs Detroit Red Wings, 18. April 2006 ” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season. This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You canFORECHECKING DATA
October 22, 2019. Daniel Weinberger Data Analysis, Forechecking Data, Passing Data Leave a comment. Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xGdefinitions).
PREDICTIVE MODELLING Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean. IDENTIFYING PLAYING STYLES WITH CLUSTERING BEHIND THE NUMBERS: WHAT MAKES A STAT GOOD The first and simplest way we analyze a statistic is in sample correlations: how two numbers relate in the same sample. Generally speaking, the two numbers we compare are the statistic in question and goals, since outscoring is the ultimate objective to hockey (no, really). We look at how these two stats compare within the same set ofgames.
ZONE TIME | HOCKEY GRAPHS As some of you know, the NHL tracked offensive zone time for two seasons, 2000-01 and 2001-02, then inexplicably stopped. As some of you also know, I have a lot of historical game data, and that includes all the zone time from these seasons. Taking those performances, and focusing on the first two periods to avoid any major score effects (or NHL COACHING CHANGES Michel Therrien has an interesting distinction in the research I’ve been doing about NHL coaching changes: he’s given me 4 instances where he and his replacement have coached 20+ games within the same season.He’s also replaced or been replaced in three of those instances by legit coaching talent – he replaced Alain Vigneault for the Montreal Canadiens in 2000-01, was replaced two years THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 Exceeding Pythagorean Expectations: Part 1. “ Nashville Predators vs Detroit Red Wings, 18. April 2006 ” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season. This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You canPASSING DATA
Visualizing passes isn’t easy in hockey. In any given KHL game, there are between 700 and 900 Passes. Somewhere between 65% to 85% are successful*. If you wanted to focus on just the successful ones, you’d have to find a way to meaningfully and concisely represent500-700 events.
PREDICTIONS
2015 Hockey Graphs Standings Predictions. October 7, 2015October 8, 2015 Matt Cane Predictions Tags: ., Clever, nhl standings, Predictions 2 Comments. As a loyal reader of Hockey Graphs you may be aware that today marks the start of the 2015-16 NHL season. This is an uncertain time in many hockey fans’ lives, and you probably have questionsSHOT QUALITY
Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics.In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoringrates.
SCORING CHANCES
This is the fifth part of a five part series. Check out Part 1, Part 2, Part 3, Part 4 here. You can view the series both at Hockey-Graphs.com and APHockey.net.. To quickly recap what I’ve covered in the first four parts of this series, I have updated the work that’s been done on Pythagorean Expectations in hockey, and am looking to find out whether teams that have the best lead-protectingNHL SHOT LOCATION
This is a sort of visual anti-shot quality argument, a demonstration of why, across these five seasons, the indisputable #1 team would shoot 9.9% while the indisputable #30 team would shoot 9.6%. Notice the horseshoe design, about where defensemen normally sit, then jump up into the play. Notice the dense cluster around the high slot. BEHIND THE NUMBERS: WHAT MAKES A STAT GOOD By MithrandirMage , via Wikimedia CommonsEvery once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind theNet.
HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. QUANTIFYING THE IMPORTANCE OF HANDEDNESS Conclusion. After performing various tests to both validate the importance of handedness with respect to the performance of d-pairings and determine exactly how important it is as a variable, it is safe to conclude that NHL teams are justified in their pursuit of a balanced shooting d-corps. Correspondingly, handedness definitely warrants a TRACKING THE RED ARMY’S PASSING Tracking the Red Army’s Passing. “ Soviet Union hockey jersey ” by Santeri Viinamäki. Licensed under CC BY-SA 4.0 via Commons. One of the ways in which the hockey analytics community can enrich the discourse is to deepen our analysis of the game. I recently broke down one incredibly famous historical team’s passing data. EXPECTED GOALS ARE A BETTER PREDICTOR OF FUTURE SCORING This piece is co-authored between DTMAboutHeart and asmean. Introduction Expected goals models have been developed in a number of sports to better predict future performance. For sports like hockey and soccer where goals are inherently random and scarce, expected goals models proved to be particularly useful at predicting future scoring. This is because they takeFORECHECKING DATA
October 22, 2019. Daniel Weinberger Data Analysis, Forechecking Data, Passing Data Leave a comment. Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xGdefinitions).
PREDICTIONS
2015 Hockey Graphs Standings Predictions. October 7, 2015October 8, 2015 Matt Cane Predictions Tags: ., Clever, nhl standings, Predictions 2 Comments. As a loyal reader of Hockey Graphs you may be aware that today marks the start of the 2015-16 NHL season. This is an uncertain time in many hockey fans’ lives, and you probably have questionsSHOT QUALITY
Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics.In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoringrates.
PREDICTIVE MODELLING Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean.DATA VISUALIZATION
Chatter Charts is a sports visualization that mixes statistics with social media data. And unlike most charts, it is specifically designed to thrive on social media; it is presented in video and filled with volatility, humour, and relatable moments. It assumes a game is like a linear story—filled with peaks and troughs—except every story is EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. TRACKING THE RED ARMY’S PASSING Tracking the Red Army’s Passing. “ Soviet Union hockey jersey ” by Santeri Viinamäki. Licensed under CC BY-SA 4.0 via Commons. One of the ways in which the hockey analytics community can enrich the discourse is to deepen our analysis of the game. I recently broke down one incredibly famous historical team’s passing data. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game.FORECHECKING DATA
October 22, 2019. Daniel Weinberger Data Analysis, Forechecking Data, Passing Data Leave a comment. Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xGdefinitions).
PREDICTIONS
2015 Hockey Graphs Standings Predictions. October 7, 2015October 8, 2015 Matt Cane Predictions Tags: ., Clever, nhl standings, Predictions 2 Comments. As a loyal reader of Hockey Graphs you may be aware that today marks the start of the 2015-16 NHL season. This is an uncertain time in many hockey fans’ lives, and you probably have questionsSHOT QUALITY
Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics.In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoringrates.
PREDICTIVE MODELLING Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. FENWICK | HOCKEY GRAPHS xSV% is a better predictor of goaltending performance than existing models. October 23, 2015. October 23, 2015. dtmaboutheart NHL League-Wide Analysis Tags: expected goals, Fenwick, Goalies, Goaltenders, Goaltending, Save percentage, Shot Quality 4 Comments. This piece is co-authored between DTMAboutHeart and asmean.DATA VISUALIZATION
Chatter Charts is a sports visualization that mixes statistics with social media data. And unlike most charts, it is specifically designed to thrive on social media; it is presented in video and filled with volatility, humour, and relatable moments. It assumes a game is like a linear story—filled with peaks and troughs—except every story is EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the HOW MUCH DO NHL PLAYERS REALLY MAKE? At the individual player level, the maximum salary is also set relative to the salary cap, and cannot exceed 20% of the upper limit ($14.6 million for 2016-17, $15 million for 2017-18). The minimum salary is adjusted on a set schedule: it was set at $575,000 in 2016-17 and boosted to $650,000 for the 2017-18 season. TRACKING THE RED ARMY’S PASSING Tracking the Red Army’s Passing. “ Soviet Union hockey jersey ” by Santeri Viinamäki. Licensed under CC BY-SA 4.0 via Commons. One of the ways in which the hockey analytics community can enrich the discourse is to deepen our analysis of the game. I recently broke down one incredibly famous historical team’s passing data. THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game.ABOUT OUR AUTHORS
Co-Founders Benjamin Wendorf, Ph.D.: Currently, Benjamin is an associate professor of history at Quinsigamond Community College in Worcester, Massachusetts. He previously worked as a freelance NHL analytics writer, most recently for The Hockey News and the Chicago Tribune. Before the establishment of Hockey Graphs, he was an editor at Gabe Desjardins' Behind the Net blog and laterPREDICTIONS
2015 Hockey Graphs Standings Predictions. October 7, 2015October 8, 2015 Matt Cane Predictions Tags: ., Clever, nhl standings, Predictions 2 Comments. As a loyal reader of Hockey Graphs you may be aware that today marks the start of the 2015-16 NHL season. This is an uncertain time in many hockey fans’ lives, and you probably have questions THE STATE OF GOALIE PULLING IN THE NHL This strategy is used almost universally in the NHL: from the 2013-14 season through 2019-20, of all game situations in which one team was trailing within the last two minutes, there was a goalie pull in 98% of those games. The other 2% of those games almost always involved a very late goal (e.g., the game was tied until the last 10 seconds, so EXPLORATORY DATA ANALYSIS USING TIDYVERSE In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data. The files and code are available on the Hockey-Graphs GitHub page. The two games we’ll use for these exercises are the same we used in the EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 2 Exceeding Pythagorean Expectations: Part 2. “ Pythagorus Algebraic Separated ” by John Blackburne. Licenced under Public Domain via Commons. This is the second part of a five part series. Check out Part 1, Part 3, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net. In Part 1, I looked at some of the NHL COACHING CHANGES Michel Therrien has an interesting distinction in the research I’ve been doing about NHL coaching changes: he’s given me 4 instances where he and his replacement have coached 20+ games within the same season.He’s also replaced or been replaced in three of those instances by legit coaching talent – he replaced Alain Vigneault for the Montreal Canadiens in 2000-01, was replaced two years IDENTIFYING PLAYING STYLES WITH CLUSTERING Identifying Playing Styles with Clustering. April 4, 2017. April 4, 2017. Ryan Stimson coaching, Exploring Context, Passing Data, Playing Styles, Shot Quality. One of the aspects of player performance that is discussed ad nauseam is chemistry. How well do certain players elevate their performance with one player or another due to some inherent THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS The Usefulness (or lack thereof) of Hit Totals. February 9, 2015. January 1, 2016. Garret Hohl NHL League-Wide Analysis. From Wikimedia Commons. The hit statistic rivals the faceoff in praise by some more traditional hockey analysts. Both statistics are also similarly over valued in terms of their impact to the game. QUANTIFYING THE IMPORTANCE OF HANDEDNESS Conclusion. After performing various tests to both validate the importance of handedness with respect to the performance of d-pairings and determine exactly how important it is as a variable, it is safe to conclude that NHL teams are justified in their pursuit of a balanced shooting d-corps. Correspondingly, handedness definitely warrants a BEHIND THE NUMBERS: WHY PLUS/MINUS IS THE WORST STATISTIC Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net. Hockey's plus/minus may be the worst statistic in hockey, although there is some debate with goalieHOCKEY GRAPHS
VISUALIZING AND ANALYZING HOCKEY AND STATISTICSMENU
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Tags: Data
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For me at least, hand tracking is 99% of the time born out ofnecessity.
The only way I am ever going to get location data for shots is if I break out a multicoloured pen and write down all the locations and numbers myself. Its isn’t however exactly the quickest process todeal with.
I actually really enjoy hand tracking is the thing, It keeps me focused on the game at hand and stops my mind from wandering. The issue comes when it’s time to digitise that information for analysis. I have written about this before over at The Ice Garden,
back when I tracked an entire season of the Australian Womens Hockey League. That season it took me around an hour of straight work to plug in every piece of information so that tableau could process it and as my life got busier, the amount of free time I could dedicate got lessand less.
The idea to force a shiny app to do something it has no right to do came out of necessity. Partially because I wanted to be able to show heat maps to the Head Coach of the local team I work with during intermission, but mostly because my Masters project consists of getting school kids ages 11+ involved in sports analytics and I really wanted them to be able to produce their own heat maps and yet I really did not want to attempt to explain the complexities of Kernel Density Charts to a collection of 12-year-olds.So here we are.
The Hockey Plotter 1.1 Continue reading → CHATTER CHARTS – VISUALIZING REAL-TIME FAN REACTIONS September 30, 2020September 30, 2020Jake
(@ChatterCharts)
Community Tags: DataVisualization ,
social media Leave acomment
Today, I’ll explain the methodology behind Chatter Charts and show you how I use statistics, R and Python to analyze hockey from a completely unexplored angle: _YOUR _point of view. I. INTRODUCING CHATTER CHARTS CHATTER CHARTS is a sports visualization that mixes statistics with social media data. And unlike most charts, it is specifically designed to thrive on social media; it is presented in video and filled with volatility, humour, and relatable moments. It assumes a game is like a linear story—filled with peaks and troughs—except every story is written by fan comments on social media. It actually tries to recreate the emotional roller coaster fans tend to experience when watching sports. But most people don’t know about the math and code behind Chatter Charts. It isn’t just me picking words I think are funny or a simple word count—it uses a topic modeling technique called TF-IDF to statistically rank them. I want to go through that with you today. Continue reading → APPLIED PROSPECT PIPELINE (APPLE): ASSISTING THE ANALYSIS OF HOCKEY PROSPECTS USING RECURRENT NEURAL NETWORKS August 7, 2020August 7, 2020connorjungle
Draft
, Prospects and DraftTags: Draft
, Draft Pick Value
, Entry Draft
, NHL Draft
, NHL Draft Pick Value, NHL Prospects
, Predictive Modelling, Prospects
2 Comments
The NHL Draft acts as the proverbial reset of the NHL calendar. Teams re-evaluate the direction of their organizations, make roster decisions, and welcome a new crop of drafted prospect into the fold. Irrespective of pick position, each team’s goal is to select players most likely to play in the NHL and to sustain success. Most players arrive to the NHL in their early 20s, which leaves teams having to interpolate what a player will be 4-5 years out. This project attempts to address this difficult task of non-linear player projections. The goal is to build a model for NHL success/value using a player’s development — specifically using all current/historical scoring data to estimate the performance of a player in subsequent seasons and the possible leagues the player is expected to be in. Continue reading → RACIAL BIAS IN DRAFTING AND DEVELOPMENT: THE NHL’S BLACK QUARTERBACK PROBLEM July 22, 2020July 22, 2020yolo_pinyato
NHL Topical Analysis, Prospects
and Draft 8
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INTRODUCTION
It is far from shocking that the National Hockey League has no peer among major American sports leagues in terms of racial homogeneity. Most estimates place the proportion of White players in the league in the range of 92-95%, far from comparable leagues like the National Football League, National Basketball Association and even Major LeagueBaseball.
In the past year, the league celebrated an obscure but rather dubious milestone. If you combined all the faceoffs* taken by every Black player** in the NHL between 2008 and 2019, you would end up with 14,375 total faceoffs, or about 20 fewer than Golden Knights center Paul Stastny in that time frame (according to Hockey-reference.com). It was only in this past season that the total of the Black playersovertook Stastny.
Continue reading → EXAMINING PLAYER DEVELOPMENT IN NCAA DI WOMEN’S HOCKEY WITH GAMESCORE PT. 2
June 26, 2020June 26, 2020Mike
Murphy
Developmental Curves, Women's
Hockey Leave a
comment
_CONTINUED FROM PT. 1_
When do women’s hockey players reach their peak? How do they develop? These questions may sound straightforward, but they are exceedingly difficult to answer because of the finite opportunities for players to pursue high-level post-collegiate hockey. There is no consensus “top” professional league in the world, and major international tournaments are brief; conclusions we draw from them can be heavily skewed by the group format. For all these reasons and more, NCAA DI (Division I) is a logical place to explore player development. It is data-rich, relative to the rest of women’s hockey, and Carleen Markey’s work with agingcurves
placed CWHL (Canadian Women’s Hockey League) skaters’ peak offensive production between the ages of 22 and 23. That falls within the range of many collegiate careers. _Credit: Carleen Markey_THE PIPELINE
The zenith of skill and competition in the world of women’s hockey are the Olympics and the IIHF Women’s World Championship. These tournaments are filled with, and often dominated by, active DI players and alumnae. As one might expect, the majority of those players represent Team USA and Team Canada. At the 2019 Worlds in Espoo, Finland, all of Team USA’s roster and 20 of the 23 players on Team Canada spent at least one year in an NCAA DI program, compared to just five of the 23 players on Team Finland’s silver medal-winning team, and one player on Team Russia’s fourth-place team. That said, there are more international players playing college hockey in North America every year. Per biographical data on EliteProspects.com, the ratio of international players in DI hockey climbed from 4.17 percent in 2015-16 to 5.07 percent in 2019-20. Those percentages don’t mean much without the context of the women’s hockey landscape across the globe. According to the IIHF , there are 88,732 registered female players in Canada and 82,808 in the U.S. Outside of North America, there are 26,381 registered players in Sweden, Finland, Czech Republic, Russia, France, Germany, Switzerland, Japan, and Norwaycombined.
Continue reading → EXAMINING PLAYER DEVELOPMENT IN NCAA DI WOMEN’S HOCKEY WITH GAMESCORE PT. 1
June 25, 2020June 26, 2020Mike
Murphy
Developmental Curves, Women's
Hockey Leave a
comment
Carleen Markey broke new ground with her presentation on women’s hockey aging curves in the CWHL (Canadian Women’s Hockey League) at RITSAC 2019. Her work, which was built from the scaffolding of the Evolving Wild twins’ aging curves,
established that offensive production among CWHL skaters peaked around age 22 to 23. That work by Markey got me thinking about how players developed just before going pro in North America and Europe, and/or becoming fixtures on national teams. So, I set my eyes on NCAA DI (Division I) women’s hockey. DI schools have served as the primary pipeline of talent for Team Canada and Team USA for decades. Furthermore, DI schools have served as a valuable proving ground for many of the most talented European players in the world. With Carleen’s work in mind, I set out to analyze how skaters developed in DI hockey before they reached their peak production years and their athletic prime.APPROACH
The greatest obstacle to any statistical analysis of the women’s game is the scarcity of public data. Fortunately, NCAA DI is something of an exception because of sites like collegehockeystats.net,
collegehockeynews.com, and the database
on HockeyEastOnline.com.
I decided on developing a game score for DI hockey to serve as an all-in-one stat that could provide a rough measure of a player’s overall impact or value. Dom Luszczyszyn first applied game score tohockey
,
and his work provided a framework. Creating game score for DI hockey was also appealing because I was able to apply lessons learned from working with Shawn Ferris’ NWHL (National Women’s Hockey League)game score
.
At the time, this sounded like fewer headaches for me. I was wrong; I had forgotten how many headaches there were the first go around. Continue reading → HOW TO DEBUG DATA SCIENCE CODEJune 22, 2020
Alex
Novet Data Analysis, Resources
Leave a comment
Think of everyone who has a talent you admire. Athletes, writers, anyone. If you were to ask each of them for the secret to their success, how many of them would be able to give the true answer? I’m not saying that they would deliberately lie. Rather, it’s just genuinely very hard to objectively assess oneself and turn natural implicit behaviors into explicit lessons that can be described toothers.
Implicit lessons can be a barrier to people learning new skills: it’s much harder to learn something if their instructor doesn’t know it’s something they ought to teach. The best teachers are able to put themselves into the shoes of their students and convey the most important pieces of information. One area of data science that is too often left implicit is troubleshooting. Everyone who writes code will get error messages. This is frustrating and can halt progress until solved. Yet most resources devoted to teaching new data scientists don’t discuss what to do, as if they’re expected to study enough to code everything correctly the first time and never encounter an unexpected error. You can find articles about common mistakes that data scientists make, but what about when you inevitably make an uncommon one? There are very few resources around how to debug broken code. (This one is quite nice, and thesetwo
are worth a read as well.) That’s what I’m hoping to partially remedy with this article. It’s far from the single canonical process for debugging, but I hope that it helps people get unstuck while they learn. The key points Iwant to convey are:
* Every data scientist hits an error messages regularly, and doing so as a new programmer is not a sign of failure * Isolate the issue by finding the smallest piece of code thatcreates the problem
* The exact language of an error message can be extremely helpful, even if it doesn’t make sense * The internet is (only in this particular instance) your friend, and there are particular resources that are particularly helpful forsolving problems
Continue reading → QUANTIFYING THE VALUE OF AN NHL TIMEOUT USING SURVIVAL ANALYSIS:PART 1
May 28, 2020
Ian
Ferer Data AnalysisTags: Mentor
Program , Timeouts
Leave a comment
_I’d like to thank Luke Benz, my mentor via the Hockey Graphs Mentorship Program, for all of his help in developing this project_.INTRODUCTION
Hockey, by nature, is a fast-paced sport that can be difficult to represent by discrete situations. While most other professional sports can be viewed as combinations of distinct in-game events – at-bats in baseball, plays and series in football, and even possessions in basketball – hockey is extremely fluid, with a constantly changing game state. This difference in game flow means that there are far fewer opportunities for a hockey coach to make any decisions based on distinct game states. While, for example, a football coach has several opportunities per game to decide whether or not to attempt a fourth-down conversion, a hockey coach has very few chances to make any comparable choice that can affect the outcome of the game. However, there are a few tools available to a hockey coach that can be researched so as to optimize their effectiveness in helping a team towin a game.
The most-researched of these decisions (thus far) for an NHL coach is when to pull the goalie in an endgame situation. There have been several papers published regarding the optimal time to pull the goalie, such as these two by Beaudoin and Swartz in 2010and by Brown and
Asness in 2018
. (For
even more great work on goalie pull times, you can check out Meghan Hall’s talk from the 2019 Seattle Hockey Analytics Conference and her Tableau dashboard,
as well as the Goalie Pull Twitter Bot created by Rob Vollman and MoneyPuck.com .) All of this prior research has found that NHL teams should pull their goalies much sooner than conventional wisdom suggests, as teams are much more likely to score to tie the game if they pull their goalie earlierrather than later.
However, beyond pulling the goalie, there are still a few more tools at a coach’s disposal. Teams are allowed to challenge goals for certain rule infractions, use a 30-second timeout during a stoppage in play, or switch goalies if the starter is having a bad game, in addition to personnel decisions regarding line combinations or matching up players against the other team. This article focuses on timeout usage, but I plan to explore the other tools in future work. Continue reading → THE STATE OF GOALIE PULLING IN THE NHLMay 18, 2020
Meghan
Hall Uncategorized
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When people ask me how to get into sports analytics, I always suggest starting with a question that they’re interested in exploring and using that question as a framework for learning the domain knowledge and the technical skills they need. I feel comfortable giving this advice because it’s exactly how I got into hockey analytics: I was curious about goalie pulling, and I couldn’t find enough data to satisfy my curiosity. There are plentyof articles
on when
teams _should_ pull their goalies, but aside from a 2015 article onFiveThirtyEight
by Michael Lopez and Noah Davis, I couldn’t find much data on when NHL teams were _actually_ pulling their goalies and if game trends were catching up to the mathematical recommendations. I presented some data on the topic at the Seattle Hockey Analytics Conference in March 2019, but the following analysis is broader and includes more seasons of data. _Data collection notes_ * All raw play-by-play data is courtesy of Evolving-Hockeyand their scraper.
* Data includes all regular season games from 2013-14 onward. All 2019-20 data is up until the season pause, through March 11, 2020. * Only the first goalie pull per team in each game is counted for the average times. For example, if a team pulled their goalie while trailing by two and then later in the game pulled their goalie again while trailing by one, only the first instance is included in the average times. All extra attacker time is counted for the scoringrates.
* More details on this data set, particularly at the team level, isavailable here
.
Continue reading → INTRODUCING NWHLE AND TRANSLATION FACTORSMay 14, 2020
Mike
Murphy NWHL
Tags: NWHL
, Translation Factors, Women's Hockey
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In April 2017, Rob Vollman tweeted out what he called “rough andpreliminary
”
translation factors for women’s hockey. At the time, I was playing around with counting stats from two years of NWHL and CWHL hockey, and wanted to develop as many tools and resources as I could to better understand the women’s game. Curious to know what the competitive landscape of post-collegiate hockey looked like in North America and elsewhere, I began to keep track of data with the intention of building on Rob’s translation factors. The world of women’s hockey in North America has changed dramatically in the three years since Rob’s tweet. My initial plans went up in smoke when the CWHL suddenly folded after the 2018-19 season. As a result, I shifted my focus to developing NWHL equivalency factors – or NWHLe – for NCAA DI, NCAA DIII, and USports. Unfortunately, it quickly became apparent that the sample size of USports alumnae to play a significant number of games in the NWHL was too small to work with. Continue reading →POST NAVIGATION
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