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FORECHECKING DATA
From past posts, we have a general sense of the basics of zone exits: zone exits are important because they get you out of your zone and towards an opportunity to score.The key to a successful zone exit is maintaining possession, ideally by avoiding the temptation to dump the puck out. But so far, we have only looked at zone exits league wide.TEAM POSSESSION
What we are calling "Team Possession" is a proxy measure taken from shots-for and against in the first two periods and expressing them as a percentage (2 shots-for + 2 shots-against = 50%), called 2-Period Shot Percentage or 2pS%. This measure has been tested and shown to be very close to our modern possession measures. FENWICK | HOCKEY GRAPHS This is part-opportunity to finally explore this question, and part-opportunity to tout some existing and upcoming data visualizations for HG. Travis Yost has been following the absolutely terrible Sabres season all year, and has raised some questions about whether it’s an all-time worst team. He’s only been able to reach back to the admittedly bad early 2000s Atlanta Thrashers, but the THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS 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. There has been work previously shown that the hitting statistic actually has a negative relationship with winning. I wanted to look into this just a little 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. QUANTIFYING THE IMPORTANCE OF HANDEDNESS A question that I have been trying to answer is the impact on a player of being asked to play on their backhand. So basically say a player spends a couple of seasons playing in a balanced pair, then either they are traded to another team, or their partner gets changed and that player finds themselves on a R/R or L/L pairing. 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. Let's take a look at what we can 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 NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK 1998-1999 was the year when things changed, not the lockout. That was the year the NHL started the two referee system. An extra pair of eyes reduced the number of cheap shots behind the play (re: the massive increase in minor penalties over the first two years). THE STATE OF GOALIE PULLING IN THE NHLFORECHECKING DATA
From past posts, we have a general sense of the basics of zone exits: zone exits are important because they get you out of your zone and towards an opportunity to score.The key to a successful zone exit is maintaining possession, ideally by avoiding the temptation to dump the puck out. But so far, we have only looked at zone exits league wide.TEAM POSSESSION
What we are calling "Team Possession" is a proxy measure taken from shots-for and against in the first two periods and expressing them as a percentage (2 shots-for + 2 shots-against = 50%), called 2-Period Shot Percentage or 2pS%. This measure has been tested and shown to be very close to our modern possession measures. FENWICK | HOCKEY GRAPHS This is part-opportunity to finally explore this question, and part-opportunity to tout some existing and upcoming data visualizations for HG. Travis Yost has been following the absolutely terrible Sabres season all year, and has raised some questions about whether it’s an all-time worst team. He’s only been able to reach back to the admittedly bad early 2000s Atlanta Thrashers, but the THE USEFULNESS (OR LACK THEREOF) OF HIT TOTALS 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. There has been work previously shown that the hitting statistic actually has a negative relationship with winning. I wanted to look into this just a little 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. QUANTIFYING THE IMPORTANCE OF HANDEDNESS A question that I have been trying to answer is the impact on a player of being asked to play on their backhand. So basically say a player spends a couple of seasons playing in a balanced pair, then either they are traded to another team, or their partner gets changed and that player finds themselves on a R/R or L/L pairing. 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. Let's take a look at what we can 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 NHL PLAYER SIZE FROM 1917-18 TO 2014-15: A BRIEF LOOK 1998-1999 was the year when things changed, not the lockout. That was the year the NHL started the two referee system. An extra pair of eyes reduced the number of cheap shots behind the play (re: the massive increase in minor penalties over the first two years).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
Passing and Zone Entries are so last year. When Corey Sznajder decided to track microstats for the upcoming season and began incorporating my passing concepts into his work on last season’s playoffs, I wondered if we really needed to track this season.Instead, Corey and I chatted a bit and decided the best use of everyone’s time would be if myself and the other passing project volunteers THE NHL PLAYOFFS AND PENALTIES Not long ago, we researched hit and face off differentials and their relationship with playoff performance, in both the same statistic and in goal performance. As a fan of the Winnipeg Jets, who lead the league for worst penalty differential for much of the season, I find it a very interesting topic to research. More power plays and EXPLORATORY DATA ANALYSIS USING TIDYVERSE This post assumes beginner knowledge of R. Welcome to the second article in our series on basic data cleaning and data manipulation! In this article, we’re going to use play-by-play data from two NHL games and answer two questions: which power play unit generated the best shot rate in each game? which defenseman played 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 Interesting stuff! Reminds me a bit of personality categorizations. I remember some Isles fans a couple years back who were upset when Grabovski was placed on a scoring line (largely because he couldn’t finish very well), but he helped move the puck in the right direction, and when he was placed with scoring talent (Tavares, Lee, Okposo, Nelson, Strome, as well as Kessel back in TOR) CF60 EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 2 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 theory behind Pythagorean Expectations and their origin 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. EXCEEDING PYTHAGOREAN EXPECTATIONS: PART 1 This is the first part of a five part series. Check out Part 2, Part 3, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net. The 2015-2016 NHL season is almost here, and our sport has come upon a new phase — arguably the third — in its analytics progression. The first 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 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
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.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 goalieFORECHECKING 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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* Washington Capitals * Winnipeg Jets II – Atlanta Thrashers USING DATA TO INFORM SHORTHANDED NEUTRAL ZONE DECISIONSApril 16, 2020
Shawn
Ferris Neutral ZoneAnalysis
Tags: Neutral Zone , Penalty Kill , powerkillLeave a comment
_The following is data is all at 4-on-5 with both goalies in their nets. A special thanks to Evolving Hockey for data and their scraper._ In March of 2019, Mike Pfeil coined the term “powerkill” at the Seattle Hockey Analytics Conference. It was much more of a small excerpt from his whole presentation, but it seemed to motivate Meghan Hall and Alison Lukan. In the coming months, Lukan would write about how the Columbus Blue Jackets utilized anaggressive approach
in their penalty killing system, while Hall would present at RITSACand OTTHAC
before they finally came together to present at the Columbus Blue Jackets Hockey Analytics Conference in February. Looking to continue researching this phenomenon, I set out to answer a few questions I had. In order to give shots some added context beyond what the NHL’s public data supplies, throughout the last few months, I tracked shot assists and where possessions leading to shots had started. As a side benefit, I was also able to filter out shots that didn’t appear to exist, were recorded incorrectly, or where the possession started at 4-on-4. In 2016, Matt Cane developed a metric to approximate penalty kill aggressiveness by combining penalty kill controlled and failed entries for, and dividing them by the entries a penalty kill faces from their opponent. The theory behind that being that penalty kills that attempt to control more entries into the offensive zone are inherently more aggressive. Hall and Lukan also found that a penalty kill’s rate of controlled entries has a strong correlation to the rate at which theytake shots.
Part of the reason these two stats have such a strong correlation is that the vast majority of shots require a zone entry. Not including rebound shots, 82% of 4v5 shots stemmed from possessions starting outside of the offensive zone over the course of the 2019-20 season. Continue reading → BY THE NUMBERS: THINKING ABOUT THE WORLD CHAMPIONSHIPS ADIFFERENT WAY
March 27, 2020March 27, 2020hayyyshayyy
NHL Topical Analysis2 Comments
_This post was co-authored by Shayna Goldman and Alison Lukan_ As part of the global response to the COVID-19 pandemic, the 2020 World Championship was cancelled. But, we still wanted to see how rosters for an international tournament with NHLers could have shaken out. While it’s easy to just put together an All Star lineup for most countries, we wanted to add a twist: each country’s roster could only include NHL players and each team had to be compliant with the 2019-20 salary cap. So what does this look like? A little bit about our process, first. Six teams will compete in our fictitious tournament: Canada, USA, Sweden, Finland, Russia, and Europe. Each roster consists of 12 forwards, six defenders, and two goaltenders. Because we were limited to NHL players, talent from outside of those core countries in Europe was combined to form one super team. Continue reading → INTRODUCING OFFENSIVE SEQUENCES AND THE HOCKEY DECISION TREEMarch 26, 2020
Thibaud
Chatel Data Analysis, Decision Science
Tags: community
post , expected goals4 Comments
If you ever work for a hockey team as an analyst, you could be facing two very recurrent questions from the coaching staff. The first one is very practical: How can analytics help us work better and faster? The second one is: What is the real contribution of each player? Meaning beyond the usual on-ice “possession” stats like Corsi or Expected Goals and individual production metrics such as shots taken, scoring chances, expected goals created, zone exits, entries, or even high-danger passes (passes that end or go through the slot). But those events were not yet statistically linked to each other. Finding a way to provide answers to both questions was my goal for the last few months, and the solution was: I needed to split the game in“Sequences”.
Video coaches often break down game tape to highlight certain plays, such as a rush-based attack or a zone exit under pressure. I wanted to do the same and divide a game in as many parts as necessary, or “Sequences”. Roughly, every time the puck changes possession between teams, a new Sequence” begins. That’s about 250 Sequencesper game.
Looking at this from the point of view of the team that owns the puck, offensive Sequences extend from the moment a team gets control of the puck and starts moving forward, to the moment she loses it for good, and it must include a shot attempt in the process to have a positive value. How does this work? Let’s say a player gets the puck back in your defensive zone, you try a zone exit but fail. Sequence starts over, there can only be one exit recorded in the Sequence. So he tries another zone exit and succeed, gets into the offensive zone, the team records a couple of shot attempts, loses the puck and if the other teams gets enough control of it to try a zone exit, it means the endof the Sequence.
How does this help? Well, the basic principle is to see the total value of a Sequence. We’re use Expected Goals as our measure of “value”. To do that, we add the Expected Goals of the shot attempts in the Sequence. For example, a Sequence with two shotattempts:
* A high danger shot: 0.23 Expected Goals * A shot from the blue line: 0.01 Expected Goals * Total Sequence value: 0.23 + 0.01 = 0.24 Expected Goals Continue reading → WHICH LEAGUE IS BEST? March 2, 2020March 2, 2020Katerina
Prospects and Draft
Leave a
comment
This work is co-authored with Madeline Gall. While scouting for some sports is straightforward (college football → NFL), scouting for the NHL can be a more arduous process. With players from over 45+ international ice hockey leagues, each with its own regulations and difficulties, how can one adequately assess the quality of a player’s performance? Comparisons between leagues are not easily made; 18 points for an eighteen year old playing against other eighteen year olds in a minor league should not be attributed the same value as 18 points for an eighteen year old playing against veterans in the NHL. There have been other attempts to account for this, including player translation variables, like that of Rob Vollman’s hockey translation factors, and Gabriel Desjardin’s NHL Equivalency Ratings (NHLe). Desjardin’s NHLe previously tackled the issue of comparing and predicting player performance for League-to-NHL transitions (moving from another league into the NHL). It was great for a quick, general comparison and certainly has its advantages (easy and quick to calculate), but there are some drawbacks to its method. For starters, it didn’t necessarily control for team quality, position, and age. Translation factors are calculated using statistics from players who have played at least 20 games in the given league before playing at least 20 in the NHL. That means there’s a lot of valuable data about these in-between transitions that aren’t beingused.
In this project, we introduce a new method for comparing and projecting player performance across leagues using an adjusted z-score metric that would account for these drawbacks. This metric controls for factors such as age, league, season, and position that affect a player’s P/PG metric, and could be applied to any league of interest. This new metric is necessary as there are many characteristics that vary from league to league. Due to the different playing styles and opponent difficulty, there is not one consistent metric to make comparable evaluations of player performance for hockey leagues around the world. Other factors such as goalie strength, penalty rates, and rink dimensions are also inconsistent across international leagues. Scenarios could occur in which players of similar strength could appear to have seemingly differentperformances.
Continue reading → AN INTRODUCTION TO R WITH HOCKEY DATA December 11, 2019December 11, 2019Meghan
Hall Data Analysis
, Resources
, Uncategorized
5 Comments
I have written a couple articles over the past few months on using R with hockey data (see hereand here
),
but both of those articles were focused on intermediate techniques and presumed beginner knowledge of R. In contrast, this article is for the COMPLETE beginner. We’ll go through the steps of downloading and setting up R and then, with the use of a sample hockey data set, learn the very basics of R for exploring and visualizing data. One of the wonderful things about using R is that it’s a flexible, growing language, meaning that there are often many different ways to get to the same, correct result. The examples below are meant to be a gentle introduction to different parts of R, but please know that this really only scratches the surface of what’s available. The code used for this tutorial (which also includes more detail and more examples) is available on our Github here.
DOWNLOADING R AND GETTING SET UP Continue reading → LATERAL PUCK MOVEMENT IN THE NZOctober 24, 2019
Daniel
Weinberger Data
Analysis , Neutral
Zone Analysis
, Passing
Data 2 Comments
Research shows that lateral/”east-west” puck movement in the offensive zone is beneficial to increasing one’s odds ofscoring
.
But I have now heard from people in various positions within the hockey industry on why it might also be useful to generate east-west puck movement in the neutral zone. The theories – focused on lateral passing, lane changes and stretch passes, respectively – all boiled down to one point: When you rush the puck up ice, the defending team will focus on that side, leaving the other side of the ice somewhat more open, so there might be open ice to exploit. Continue reading → PASSING CLUSTERS: A FRAMEWORK TO EVALUATE A TEAM’S BREAKOUT October 22, 2019October 22, 2019Daniel
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 xG definitions). Therefore, understanding what does and does not work when breaking out the puck against present forecheckers is important. There is evidence that passes from the defensive half boards by wingers inside produce more offense than those straight up ice. But the puck is more often recovered elsewhere, so these passes by wingers aren’t the first pass in a possession and are therefore presumably influenced by the previous play. It should be interesting to find out how the inclusion of the pass(es) that came before affects this conclusion. Continue reading → A CROWDFUNDING INITIATIVE TO PROMOTE DIVERSITY AT THE COLUMBUS ANALYTICS CONFERENCE October 21, 2019November 7, 2019pflynnhockey
Community
, Conferences
, Uncategorized
Leave a comment
I’ve been fortunate enough to be able to attend the last three years of the RIT Sports Analytics Conference. The first year I went, I was nervous to meet people whose work I admired. I was afraid that nobody would want to talk to this new person that few people knew and who was just starting to learn about the field. I could not have been more wrong. Continue reading → EXPLORATORY DATA ANALYSIS USING TIDYVERSE October 8, 2019October 9, 2019Meghan
Hall Data Analysis
, Exploring Context
1 Comment
_This post assumes beginner knowledge of R._ Welcome to the second article in our series on basic data cleaning and data manipulation! In this article, we’re going to use play-by-play data from two NHL games and answer two questions: * which power play unit generated the best shot rate in each game? * which defenseman played the most 5v5 minutes in each game? 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. Continue reading → 2019 #RITSAC SLIDES AND VIDEO September 21, 2019September 21, 2019Ryan
Stimson
Uncategorized
Leave a comment
Below are the presenters and slides from the 2019 Sports Analytics Conference held at the Rochester Institute of Technologyon September
14th. All videos are time-stamped save for one which could not be recorded. Please reach out to Ryan Stimson if you have any difficulties accessing the slides and/or video. Enjoy! Thanks to everyone who attended and/or presented! Continue reading →POST NAVIGATION
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