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Showing posts from 2007

Does Momentum from Previous Games Predict Who Wins Game 5?

Happy holidays to everyone! Today's entry looks at whether, in a five-game match, the winner of the fifth game can be predicted by the pattern of how the first four games have gone. For example, if one team wins the first two games, but the other team wins the third and fourth games, one might expect the latter team to win the fifth game, owing to its momentum from Games 3 and 4. This line of reasoning led many to expect a Stanford win in Game 5 of the recent NCAA women's final against Penn State, but it was the Nittany Lions prevailing. In the analysis below, I looked at 2007 within-conference matches from four major women's conferences: the Big 10, Big 12, Pac 10, and SEC. As can be seen, in the 34 total matches in which one team (represented by "A") had won the first two games and the other team ("B") had rebounded to win Games 3 and 4, Team A won Game 5 -- and the match -- somewhat more often than did Team B, 19 compared to 15. Another sc

Subtlety and Context in Interpreting Volleyball Stats

Over at the VolleyTalk discussion website, user “38 Skynyrd” started a topic the evening of December 15, after the Penn State-Stanford NCAA title match, about volleyball statistics. The initial salvo in the discussion essentially argued for more subtlety and context in interpreting volleyball statistics, given that: “…absolute raw numbers in the box score do not always tell the true story of how good or bad a player did in a given game or match.” The full set of messages is available here . A number of suggestions were made by the discussants for new statistics. Given the obvious relevance of this discussion for our mission here at VolleyMetrics, I have excerpted a number of these ideas (with the author of each one credited in parentheses). These are shown below: “…a hitter may have hit for good numbers overall, but if they made 5-6 hitting errors at critical points in the match, then their overall hitting percentage may still look good in the box score, but they still had a

Top NCAA Women's Programs 2003-07, Relative to National Tourney Seedings

With another women's NCAA Division I season on the books -- Penn State having defeated Stanford in a five-game final -- now is a good time to take stock of how the nation's leading programs have been doing in NCAA play in recent years. As one option, we could look at which schools have been winning championships and making the Final Four. Looking at the last five years, we would find many "usual suspects," such as Stanford, Nebraska, Washington, USC, and Penn State; even Minnesota, whose icy cold locale doesn't necessarily suggest volleyball greatness, has made two Final Fours in this timeframe. A more subtle approach, however, is to look at which teams have done best relative to their seedings . Such an analysis can tell us which teams raise their games come tournament time, compared to what their regular-season performance would have suggested. A team that comes into the NCAA tourney as a No. 16 national seed, for example, would be favored to win two mat

"Natural Experiment" Compares How Texas Tech Hit Before and After Change of Setters

The women's volleyball team at Texas Tech University, where I'm on the faculty, just finished a Big 12 season that seems hard to describe as anything other than a disaster. After winning their conference opener against Colorado, the Red Raiders lost all 19 of their remaining Big 12 matches (Oklahoma State does not field a team in this sport, meaning that each partcipating school faces 10 opponents, twice each). In Texas Tech's final 11 matches, it only won one game (i.e., 10 times it was swept 3-0 and once lost by a 3-1 score). Above is a photo I took at Tech's home match against Iowa State, relatively early in conference play. Among the misfortunes experienced by the Raiders, senior setter Emily Ziegler went out -- for the season, it turned out -- after the eighth Big 12 match with a foot injury that required surgery ( here and here ). Replacing Ziegler for the Raiders' remaining 12 conference matches was Kourtney Dunnam , whose path to the Texas Tech s

Perfect Hitting Night for Nebraska's Tracy Stalls

The story coming out of the University of Nebraska volleyball building tonight, quoting from this news release , is that: Tracy Stalls tied an NCAA record by putting down 13 kills on 13 swings for a perfect 1.000 attack percentage, as the Husker volleyball team sent Stalls and NU's three other seniors out in style Saturday night with a 30-18, 30-10, 30-11 sweep of Texas Tech. For those not all that familiar with volleyball statistics, what this means is that 13 times the ball was set up for Stalls to swing at, and all 13 times she delivered balls that the Red Raiders could not field. Stalls hit no balls into the net, nor out of bounds, had no balls blocked back in her face by Texas Tech, and did not even have any balls dug up by the Red Raiders in the backcourt. Now, that's a hot hand! [Cross-posted at my Hot Hand blog, for the study of sports streakiness.]

Home-Court Advantage in Volleyball

One of the most widely discussed phenomena across a number of sports is the home-court (or home-field) advantage (HFA). According to Cecil Adams, who fields readers' questions on a website called The Straight Dope : Perusing a comprehensive recent study ("Long-term trends in home advantage in professional team sports in North America and England [1876-2003]," Pollard and Pollard, 2005), I note as follows: (a) since 1900, notwithstanding some year-to-year swings, MLB home-field winning percentages have been remarkably stable at about .540 ; (b) the NFL HFA fluctuates a lot, no doubt because fewer games means more statistical noise, but home-field wins are usually in the 55 to 60 percent range; (c) NHL home-ice wins have declined from 60 percent in the 70s to a pretty steady 55 percent since the mid-90s; (d) NBA home-court wins dropped from 65 percent in the mid-80s to 60 percent in recent years, still the highest of the U.S. sports studied; and (e) HFA shows up in UK

NESSIS Presentation on Volleyball

Just about a month ago, Harvard hosted the inaugural New England Symposium on Statistics in Sports ( official site , news release ). In perusing the abstracts of conference papers (which can be accessed through the conference website, where it says "Program"), I came across a study (presented in poster format) entitled, "Skill Importance in BYU Women’s Volleyball: A Bayesian Approach." The authors were BYU statistics graduate student Lindsay Florence and professor Gilbert Fellingham . The beginning of the abstract gives the basics of the study: The BYU womens volleyball team recorded all skills (pass, serve-receive, set, etc.), rated each skill, and recorded rally outcomes (point for BYU, rally continues, point for opposition) for the entire 2006 home volleyball season. Only sequences of events occurring on BYU's side of the net were considered. Florence and Fellingham were nice enough to e-mail me a PDF of their poster. It conveys some basic statistic

Overview of Defense: Blocking and Digging

Continuing our series on the different skills and facets of volleyball, our topic today is defense against opponents' spike attempts, namely blocking and digging . As always, it's a good idea to look at the formal definitions of these plays, for statistical purposes. According to AVCA guidelines , blocks "are awarded when a player blocks the ball to the opposition's court leading directly to a point without a successful dig." As elaborated in the guidelines, blocks are credited as solo or assist, according to certain criteria. Also, the hitter who is blocked in the manner described above receives an attack error. A dig "is awarded when a defensive player keeps a bona fide attack in play with a pass." The central inquiry motivating this blog, of course, is what can be learned through measurement and statistics that tells us about winning matches. Given the suggestion in an earlier posting that a team's hitting percentage seems to be a good

JQAS Article on Serve Reception, Setting, and Attack

A new issue of the online publication, the Journal of Quantitative Analysis in Sports , was announced today. Among the articles was one on volleyball by researchers from Greece, entitled "Does Effectiveness of Skill in Complex I Predict Win in Men’s Olympic Volleyball Games?" The authors made a terminological distinction between "complex I (serve reception, setting, attack)" and "complex II (serve, block/defense, counterattack)" sequences, and focused on analyzing the former. Raters evaluated videotaped game footage with a software system, issuing grades (on a 0-4 scale) on serve reception and first attack (setting was not graded). Not surprisingly, high-level execution of both reception and attack were associated with winning. The authors used discriminant analysis , which is among the more complex techniques in the data analyst's arsenal. I would have liked to see more basic statistics, such as means and frequencies with, respectively, t-tests

Overview of Serving and Serve Receipt

Today, let's take up serving and serve receiving , which appear to be two sides of the same coin. Box-score statistics tend to be quite limited, generally reporting only service aces and errors, and serve reception errors. Jim Coleman's chapter in Shondell and Reynaud's Volleyball Coaching Bible (which I've referenced previously) summarizes some schemes for grading serves and serve reception. The schemes appear to have both a spatial component -- with short serves, in the center of the receivers' court on the left-right dimension, being considered poor for the server and advantageous for the receiver, and deep serves the opposite -- and a component for how likely the receiving team would be to generate an attack for a side-out, given the placement of the ball. Consistent with calls for better statistical graphics in volleyball, I had been thinking of serve placement/receipt charts, modeled after shot charts in basketball (see examples here and here ). A

Overview of Setting

Following up on the previous entry about hitting, we now take up another indispensable part of the offense, the setting . This Daily Californian article from about a year ago describes the setter's role through the eyes of Cal-Berkeley setter Samantha Carter, who at the time was finishing up her four-year career leading the Golden Bears' offense. The following excerpts give an idea of what being a setter entails: “You have to be one of the better athletes — you’re doing more running and jumping than anyone else on the team,” says Bears coach Rich Feller... “You have to be a sponge and be able to absorb other players’ mistakes and take it upon yourself to make things better.” ... Before each play, Carter will make eye contact with all of her hitters and signal to them to designate where they’ll each be going and what the play is. Throughout the play, she vocally communicates with her teammates on the court. “First thing, when I give my calls I basically try to think,

Overview of Hitting Percentage

For my next few postings, I would like to provide initial examinations of the major volleyball statistics, to try to get a feel for them. Let's start with hitting percentage (also called attack percentage), which can be computed for either individual players or teams. For a player or a team, one totals the number of kills (i.e., successfully putting the ball away on an attempted attack), then subtracts the number of hitting errors (e.g., attack attempts hit out of bounds or into the net, or that get blocked back into the hitter's face for an opponent's point). The remaining number is then divided by total attack attempts. These terms are defined rigorously on this document from the American Volleyball Coaches Association. Imagine the following different hypothetical performances. One player, whom we might call "Dana Devastator" has the ball set up for her 10 times and successfully puts it away all 10 times. That would be a 1.000 performance ([10-0]/10)

Welcome and Introduction

Welcome to my latest blog, VolleyMetrics. The basic aim of this site is to apply the kind of analytical and statistical reasoning that has come to be known as " sabermetrics " to volleyball. The term sabermetrics derives from SABR, the Society for American Baseball Research , but increasingly is used used to refer to quantitative analysis of sports in general. Phil Birnbaum's Sabermetric Research Blog is devoted to all sports, for example. Three things about me make a sabermetric volleyball site a logical next step: 1. I am a member of SABR and am very much in tune with the sabermetric approach. Another of my blogs, The Hot Hand , is devoted to the statistical study of sports streakiness. 2. I am a professor at Texas Tech University in the Department of Human Development and Family Studies. Although my primary substantive research area is adolescent and young-adult drinking (and personal development during this part of the lifespan more generally), my teac