A while back, I joined a Yahoo discussion group called VolleyStats. As a result, I've been receiving e-mails from the group that fall into two categories: junk messages and serious reports of statistical analysis from someone named Leo van Hal. Because van Hal's reports are written in Dutch, I really didn't learn anything more about volleyball from them than I did from the junk e-mails.
That situation recently changed, however. I found a new Google application website (new to me, at least) called Google Translate, which is quite easy to use. You simply type (or copy and paste) text from the originating language into a box, select the "from" and "to" languages, and click! van Hal's reports often contain graphs, so rather than copy and paste his entire paper into Google Translate, I do it a few paragraphs at a time, avoiding the graphs. The translations aren't always perfect, sometimes leaving me with odd English constructions. With a little inference ("reading between the lines"), however, I'm confident that I'm picking up the key points.
One message I received was titled "[volleystats] Digest Number 447 [2 Attachments]," dated May 17, 2009. The two attached analyses were titled "Punten verdeling" (Distribution of points) and "Winst Kansen" (Profit Opportunities). Both reports have to do with testing different probabilities of teams' winning points on their serves, and how this affects the probability of different point totals in the games and teams' probabilities of winning the games. (In the English translations, the Dutch word "opslag" appears in English as "storage," but apparently "service" is another synonym; the passages will read much easier in English if you substitute "service" for "storage.")
If you would like English-translated copies of these two van Hal reports, please e-mail me via my faculty webpage in the upper-right portion of this page.
Friday, July 3, 2009
Sunday, May 10, 2009
As most people looking at this blog would already be aware, the University of California, Irvine (UCI) defeated the University of Southern California (USC) in an exciting five-game NCAA men's championship match last night. Adding to the drama and excitement was the story of the Trojans' late-season turnaround, from a fifth-place finish in the Mountain Pacific Sports Federation, to a juggernaut that swept through the MPSF postseason tournament to capture an automatic bid in the four-team NCAA tournament and overpowered Penn State in the national semifinals.
The reason for USC's recent success was no secret -- it was killer hitting, especially by 6-foot-8 sophomore Murphy Troy. Whether Troy was in the front row or back row (where a player can still hit, as long as he takes his jump from behind the 10-foot line), USC would frequently set the ball to him, and Troy would deliver. Troy's hitting statistics in SC's last five matches, in terms of number of kills and hitting percentage were as follows: 28/.380 vs. Stanford, MPSF; 19/.600 vs. UCI, MPSF; 23/.220 vs. Pepperdine, MPSF; 24/.595 vs. Penn State; NCAA; and 26/.367 vs. UCI, NCAA.
Among the tactics an opposing team could try against a hot-hitting team, two would be tough serving (to disrupt the passing to the setter, and perhaps the sets to the hitters) and finding a way to block better than you ever have, by getting two (or even three) players up against the opponent's top hitters.
As shown in the following chart of key statistics from USC's postseason matches (which you can click to enlarge), the high rate of service errors by Trojan opponents suggests that they may have been opting for the aggressive serving tactic. Penn State, with 21 such errors in only four games, stands out in this regard (although the elevated altitude in Provo, Utah, which would cause the ball to carry further, also could have been a factor).
In the national championship match, UCI had only 11 service errors (very low, considering the match went five games). More importantly, though, the Anteaters greatly increased their blocking productivity.
I've read several online articles this afternoon about last night's match, but I haven't been able to find any quotes from UCI Coach John Speraw regarding strategies he employed so that his team's block could be so effective against USC. The best I could find was: "We did a much better job of taking away their tendencies" (from a list of postgame quotes on the UCI athletics site). Comments from any "X's and O's" people would be welcome!
The reason for USC's recent success was no secret -- it was killer hitting, especially by 6-foot-8 sophomore Murphy Troy. Whether Troy was in the front row or back row (where a player can still hit, as long as he takes his jump from behind the 10-foot line), USC would frequently set the ball to him, and Troy would deliver. Troy's hitting statistics in SC's last five matches, in terms of number of kills and hitting percentage were as follows: 28/.380 vs. Stanford, MPSF; 19/.600 vs. UCI, MPSF; 23/.220 vs. Pepperdine, MPSF; 24/.595 vs. Penn State; NCAA; and 26/.367 vs. UCI, NCAA.
Among the tactics an opposing team could try against a hot-hitting team, two would be tough serving (to disrupt the passing to the setter, and perhaps the sets to the hitters) and finding a way to block better than you ever have, by getting two (or even three) players up against the opponent's top hitters.
As shown in the following chart of key statistics from USC's postseason matches (which you can click to enlarge), the high rate of service errors by Trojan opponents suggests that they may have been opting for the aggressive serving tactic. Penn State, with 21 such errors in only four games, stands out in this regard (although the elevated altitude in Provo, Utah, which would cause the ball to carry further, also could have been a factor).
In the national championship match, UCI had only 11 service errors (very low, considering the match went five games). More importantly, though, the Anteaters greatly increased their blocking productivity.
I've read several online articles this afternoon about last night's match, but I haven't been able to find any quotes from UCI Coach John Speraw regarding strategies he employed so that his team's block could be so effective against USC. The best I could find was: "We did a much better job of taking away their tendencies" (from a list of postgame quotes on the UCI athletics site). Comments from any "X's and O's" people would be welcome!
Thursday, February 19, 2009
Chuck Rey, volunteer assistant coach at the University of Minnesota, e-mailed me this morning with a statistical variation on hitting percentage.
Rather than putting the emphasis on kills, which the usual measure of hitting percentage does, the Gophers focus on putting the ball in play (i.e., getting a kill or at least not spiking the ball out of bounds or into the net). They even have a name for it, the "In Play Efficiency" or IPE. As Chuck stated in his e-mail to me, the "IPE stat is just a 1 - the error %."
In addition to the Hitting IPE, there's also "Total IPE," where the error rate is based on "all team errors commited (blocks errors, hitting errors, service errors, ball handling errors, passing errors)."
Chuck was kind enough to present me with season-long IPE statistics for the Final Four NCAA women's teams from last December, which I am posting with his permission.
Chuck also has his own website at http://www.coachrey.com .
Rather than putting the emphasis on kills, which the usual measure of hitting percentage does, the Gophers focus on putting the ball in play (i.e., getting a kill or at least not spiking the ball out of bounds or into the net). They even have a name for it, the "In Play Efficiency" or IPE. As Chuck stated in his e-mail to me, the "IPE stat is just a 1 - the error %."
In addition to the Hitting IPE, there's also "Total IPE," where the error rate is based on "all team errors commited (blocks errors, hitting errors, service errors, ball handling errors, passing errors)."
Chuck was kind enough to present me with season-long IPE statistics for the Final Four NCAA women's teams from last December, which I am posting with his permission.
Chuck also has his own website at http://www.coachrey.com .
Sunday, December 21, 2008
As virtually all college-volleyball fans would know by now, Penn State has successfully defended its NCAA women's title and gone undefeated (38-0) in the process, sweeping Stanford in three games in the final. The scores were 25-20, 26-24, and 25-23. Here are a few brief observations on the match...
Penn State outhit Stanford in Games 1 (.257-.167) and 3 (.196-.109). The Cardinal outhit the Nittany Lions in Game 2, .159-.102. As I showed in analyses of the earlier rounds, a team will usually win the game when it outhits its opponent by this much. In this instance, though, it didn't turn out that way for Stanford.
On the ESPN2 telecast of the final, color commentator Karch Kiraly added a statistical flavor to the proceedings, with his periodic evaluations of the teams' serve-receipt/passing effectiveness on a 0-3 scale.
Happy holidays to everyone! VolleyMetrics will be back in the new year to focus on men's college volleyball.
Penn State outhit Stanford in Games 1 (.257-.167) and 3 (.196-.109). The Cardinal outhit the Nittany Lions in Game 2, .159-.102. As I showed in analyses of the earlier rounds, a team will usually win the game when it outhits its opponent by this much. In this instance, though, it didn't turn out that way for Stanford.
On the ESPN2 telecast of the final, color commentator Karch Kiraly added a statistical flavor to the proceedings, with his periodic evaluations of the teams' serve-receipt/passing effectiveness on a 0-3 scale.
Happy holidays to everyone! VolleyMetrics will be back in the new year to focus on men's college volleyball.
Saturday, December 20, 2008
With tonight's NCAA championship match between Penn State and Stanford just hours away, I wanted to provide some pregame statistical analysis. In the aftermath of Penn State's dramatic semifinal victory over Nebraska, one of the most frequent observations among discussants at the VolleyTalk site was how the Nittany Lions appeared to stray from attacking the middle against the Cornhuskers. In order to break things down scientifically, I created the following graph based on Penn State's NCAA tournament matches thus far this season.
Indeed, it appears that the Nittany Lions' three middle-hitters (Christa Harmotto, Blair Brown, and Arielle Wilson) have been getting a declining proportion of the team's hitting attempts in recent matches.
Meanwhile, outside-hitter Nicole Fawcett has, to an increasing extent, been getting the "lion's share" of the hit attempts. Penn State's other main outside-hitter, Megan Hodge, has consistently been getting between 25-35% of the team's hitting attempts in the tournament, thus the degree to which she was set against the Cornhuskers was well within normal range.
From Stanford's perspective, one of the main ways in which the Cardinal has succeeded this season is by limiting opponents' offensive prowess. When that fails, however, Stanford seems to be able to lift its own offense to a higher level. This game article from Stanford's semifinal victory over Texas notes that, "The Longhorns were the first team all year to hit above .300 against the Cardinal..."
In fact, even with the Longhorns hitting .438 and .381, respectively, in Games 4 and 5, the Cardinal was able to prevail in both games by hitting an astronomical .439 and .500 (box score). As discussed in the article, it was Stanford's "Big Three" doing the damage:
It took all that Stanford's three All-American hitters could muster, but in the end Alix Klineman, Cynthia Barboza and Foluke Akinradewo played one of the best matches of their careers. Klineman paced the team with 20 kills, while Akinradewo slammed 17 on .452 hitting and provided six critical blocks. It was Barboza, however, who stole the show in the improbable comeback, recording 15 of her 19 kills in the final three sets.
In conclusion, Stanford appears to have two options: slowing down Penn State's offensive attack or, failing that, prevailing in a slugfest. Neither seems too likely to me.
Indeed, it appears that the Nittany Lions' three middle-hitters (Christa Harmotto, Blair Brown, and Arielle Wilson) have been getting a declining proportion of the team's hitting attempts in recent matches.
Meanwhile, outside-hitter Nicole Fawcett has, to an increasing extent, been getting the "lion's share" of the hit attempts. Penn State's other main outside-hitter, Megan Hodge, has consistently been getting between 25-35% of the team's hitting attempts in the tournament, thus the degree to which she was set against the Cornhuskers was well within normal range.
From Stanford's perspective, one of the main ways in which the Cardinal has succeeded this season is by limiting opponents' offensive prowess. When that fails, however, Stanford seems to be able to lift its own offense to a higher level. This game article from Stanford's semifinal victory over Texas notes that, "The Longhorns were the first team all year to hit above .300 against the Cardinal..."
In fact, even with the Longhorns hitting .438 and .381, respectively, in Games 4 and 5, the Cardinal was able to prevail in both games by hitting an astronomical .439 and .500 (box score). As discussed in the article, it was Stanford's "Big Three" doing the damage:
It took all that Stanford's three All-American hitters could muster, but in the end Alix Klineman, Cynthia Barboza and Foluke Akinradewo played one of the best matches of their careers. Klineman paced the team with 20 kills, while Akinradewo slammed 17 on .452 hitting and provided six critical blocks. It was Barboza, however, who stole the show in the improbable comeback, recording 15 of her 19 kills in the final three sets.
In conclusion, Stanford appears to have two options: slowing down Penn State's offensive attack or, failing that, prevailing in a slugfest. Neither seems too likely to me.
Wednesday, December 17, 2008
For the last few weeks, I've been trying to think of new ways to measure serving effectiveness. Box scores and statistical summaries typically report only service aces and errors (example). My concerns are that aces occur infrequently (limiting their statistical usefulness), and that focusing on aces does not take into account how even serves that are picked up by the receiving team can still be advantageous to the serving team (e.g., by preventing the receiving team from setting up its top available hitting threat).
Alternatively, one can obtain detailed statistics by observing and classifying the receiving outcomes of serves into micro-level categories, such as whether a serve disrupted the receiving team's ability to "mak[e] the first tempo attack," as reported in this article. If a team has the staffpower resources to record such statistics, that's great, but not everyone can.
What I've been conceiving of, therefore, is some sort of "middle ground" statistic -- something easily derivable from online play-by-play sheets (as can be accessed, for example, via this NCAA interactive bracket by clicking on particular games), but that goes beyond just service aces and errors. What I've come up with is the Length of Average Serving Stint or LASS.
The longer a serving stint, the more points the serving team is racking up. If a stint lasts for one serve, the serving team has not received a point (i.e., the other team has sided-out). If a serving stint lasts two serves, the serving team has garnered one point. If a stint lasts three serves, the serving team has accumulated two points, etc. Thus, longer serving stints appear to capture -- indirectly, at least -- effective serving.
Before anyone starts sending me e-mails of complaint, I am aware that the identity of a server is systematically connected to (or "confounded" with) the serving team's front-court line-up, due to the rotation. Thus, if Player A tends to have long serving stints, some (or even most) of the credit might be due to the team's having Players B, C, and D in the front court, rather than Player A's vicious serving. I never claimed that my new statistic was perfect!
Also, I suspect that many coaches already chart their teams' success at winnning points and siding-out, by rotation, which is very similar to my scheme. The difference would just be a matter of focus, as I'm interested in who is serving.
What the LASS does have going for it, however, is relative ease of compilation. One can simply look at a play-by-play sheet and see how many plays in a row somebody served. A sample chart of LASS statistics is shown below, for the University of Texas in its recent NCAA Elite Eight match-up against Iowa St. Shown in each box is the length of a given serving stint; going down the first column shows you each player's first stint (in the order they served), the second column shows each player's second stint, etc. You can click on these graphics to enlarge them.
I've gone ahead and calculated LASS statistics for all regular players from the Final Four teams that will be playing Thursday night, based on each team's two games in last weekend's regionals.
If one were going to adopt the LASS, it would be best to use a much larger database than just two matches; my initial calculations were purely for illustrative purposes.
I'm interested in what readers think are the pros and cons of the LASS. I invite you to use the Comments section to provide feedback. Now enjoy the Final Four!
Alternatively, one can obtain detailed statistics by observing and classifying the receiving outcomes of serves into micro-level categories, such as whether a serve disrupted the receiving team's ability to "mak[e] the first tempo attack," as reported in this article. If a team has the staffpower resources to record such statistics, that's great, but not everyone can.
What I've been conceiving of, therefore, is some sort of "middle ground" statistic -- something easily derivable from online play-by-play sheets (as can be accessed, for example, via this NCAA interactive bracket by clicking on particular games), but that goes beyond just service aces and errors. What I've come up with is the Length of Average Serving Stint or LASS.
The longer a serving stint, the more points the serving team is racking up. If a stint lasts for one serve, the serving team has not received a point (i.e., the other team has sided-out). If a serving stint lasts two serves, the serving team has garnered one point. If a stint lasts three serves, the serving team has accumulated two points, etc. Thus, longer serving stints appear to capture -- indirectly, at least -- effective serving.
Before anyone starts sending me e-mails of complaint, I am aware that the identity of a server is systematically connected to (or "confounded" with) the serving team's front-court line-up, due to the rotation. Thus, if Player A tends to have long serving stints, some (or even most) of the credit might be due to the team's having Players B, C, and D in the front court, rather than Player A's vicious serving. I never claimed that my new statistic was perfect!
Also, I suspect that many coaches already chart their teams' success at winnning points and siding-out, by rotation, which is very similar to my scheme. The difference would just be a matter of focus, as I'm interested in who is serving.
What the LASS does have going for it, however, is relative ease of compilation. One can simply look at a play-by-play sheet and see how many plays in a row somebody served. A sample chart of LASS statistics is shown below, for the University of Texas in its recent NCAA Elite Eight match-up against Iowa St. Shown in each box is the length of a given serving stint; going down the first column shows you each player's first stint (in the order they served), the second column shows each player's second stint, etc. You can click on these graphics to enlarge them.
I've gone ahead and calculated LASS statistics for all regular players from the Final Four teams that will be playing Thursday night, based on each team's two games in last weekend's regionals.
If one were going to adopt the LASS, it would be best to use a much larger database than just two matches; my initial calculations were purely for illustrative purposes.
I'm interested in what readers think are the pros and cons of the LASS. I invite you to use the Comments section to provide feedback. Now enjoy the Final Four!
Thursday, December 11, 2008
I've put together a bunch of statistics on last weekend's opening two rounds of play in the NCAA Division I women's volleyball tournament. Forty-eight matches were played (32 in the first round and 16 in the second), comprising roughly three-fourths of the tournament's total matches (63 are played, in all).
In these 48 matches, 179 total games (sets) were played. The type of result (i.e., sweeps, four- and five-game matches) broke down as follows:
3-0: 24
3-1: 13
3-2: 11
The closeness of many matches is illustrated by looking more closely at the five-game tilts. Five were decided by the minimum two points, another three were decided by the score of 15-12, and only three were decided by 5 or more points.
Regular readers of this site know that I consider hitting percentage to be a very important statistic. For each of the 179 individual games played over the first weekend, I examined each team's hitting percentage in relation to who won the game. In only 19 games (11%) did the lower-hitting team win the game.
The following chart shows the relationship between the margin by which the higher hitting team in a game outhit the lower hitting team (horizontal axis) and the probability of the higher hitting team winning the game (vertical axis).
Starting at the left, when a team outhit its opponent by a very small amount (.001-.049), it had about a 62% chance of winning the game (16/26, which is not significantly above a 50/50 chance probability). If a team outhit its opponent by a somewhat larger margin (.050-.099), it had a 78% chance of winning the game (25/32, which is significantly beyond chance).
The remaining bars in the graph tell us that, if one team's hitting percentage in a game is greater than its opponent's by .100 or more, the higher-hitting team was virtually certain to win the game. In fact, from this point onward, there were only two cases (out of 121 possible) where a team outhit its opponent and lost.
One of these instances occurred in Game 1 of the Illinois-Cincinnati match in the second round. Cincinnati recorded the better hitting percentage (.294 vs. .189, a difference of .105), but Illinois prevailed 26-24.
An even more extreme anomaly occurred in Game 3 of the second-round match between Florida and Colorado State. The Gators were victorious, 25-23, despite being outhit by the substantial margin of .226 (UF .107, CSU .333). For this one, I had to see what happened, so I consulted the online play-by-play sheet. The apparent reason why the Rams lost this game despite a hefty hitting advantage is that they made EIGHT service errors.
I also looked at some miscellaneous hitting statistics. Two teams stood out as super-consistent in particular matches, their game-to-game hitting percentages staying within a band of .100 percentage points throughout five games.
In a first-round win over San Francisco, Duke recorded the following hitting percentages in the five games: .255, .243, .243, .182, and .273 (box score).
Also in the opening round, Purdue hit for the following percentages in defeating Louisville: .333, .290, .333, .321, and .294 (box score).
I hope these statistics will give you something to think about as you await the next round, beginning Friday.
In these 48 matches, 179 total games (sets) were played. The type of result (i.e., sweeps, four- and five-game matches) broke down as follows:
3-0: 24
3-1: 13
3-2: 11
The closeness of many matches is illustrated by looking more closely at the five-game tilts. Five were decided by the minimum two points, another three were decided by the score of 15-12, and only three were decided by 5 or more points.
Regular readers of this site know that I consider hitting percentage to be a very important statistic. For each of the 179 individual games played over the first weekend, I examined each team's hitting percentage in relation to who won the game. In only 19 games (11%) did the lower-hitting team win the game.
The following chart shows the relationship between the margin by which the higher hitting team in a game outhit the lower hitting team (horizontal axis) and the probability of the higher hitting team winning the game (vertical axis).
Starting at the left, when a team outhit its opponent by a very small amount (.001-.049), it had about a 62% chance of winning the game (16/26, which is not significantly above a 50/50 chance probability). If a team outhit its opponent by a somewhat larger margin (.050-.099), it had a 78% chance of winning the game (25/32, which is significantly beyond chance).
The remaining bars in the graph tell us that, if one team's hitting percentage in a game is greater than its opponent's by .100 or more, the higher-hitting team was virtually certain to win the game. In fact, from this point onward, there were only two cases (out of 121 possible) where a team outhit its opponent and lost.
One of these instances occurred in Game 1 of the Illinois-Cincinnati match in the second round. Cincinnati recorded the better hitting percentage (.294 vs. .189, a difference of .105), but Illinois prevailed 26-24.
An even more extreme anomaly occurred in Game 3 of the second-round match between Florida and Colorado State. The Gators were victorious, 25-23, despite being outhit by the substantial margin of .226 (UF .107, CSU .333). For this one, I had to see what happened, so I consulted the online play-by-play sheet. The apparent reason why the Rams lost this game despite a hefty hitting advantage is that they made EIGHT service errors.
I also looked at some miscellaneous hitting statistics. Two teams stood out as super-consistent in particular matches, their game-to-game hitting percentages staying within a band of .100 percentage points throughout five games.
In a first-round win over San Francisco, Duke recorded the following hitting percentages in the five games: .255, .243, .243, .182, and .273 (box score).
Also in the opening round, Purdue hit for the following percentages in defeating Louisville: .333, .290, .333, .321, and .294 (box score).
I hope these statistics will give you something to think about as you await the next round, beginning Friday.
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