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Showing posts from January, 2012

Analysis of BYU Men's Two Wins Over USC

Because conference play gets underway very quickly in men's college volleyball (usually just after an early-season tournament or two) and the top teams tend to be concentrated in the Mountain Pacific Sports Federation (MPSF), fans don't have to wait very long for marquee match-ups to take place. Just this past weekend, No. 1 BYU hosted No. 5 USC for a pair of matches (BYU and Hawai'i always play a given opponent in a two-match home series or road series, presumably to cut down on travel). Also, No. 3 UCLA hosted No. 4 Stanford. I will focus on the BYU-USC series, as better inferences can be made from two matches than from one. BYU won both matches, but each was highly competitive. The Cougars took Friday night's first match in four games (sets), and Saturday night's rematch in five (15-13 in the fifth, in fact). As is customary, I stress hitting percentages and the teams' allocation of spike attempts. The following graphic (on which you can click to en

Hot Hand in Volleyball?

Science News has just published an article on research by German and Austrian investigators purporting to document a hot hand in volleyball spiking, and the reporter was nice enough to contact me for comment. (I operate another blog , on the statistical study of sports streakiness, and even have a book out on the subject.) A hot hand in this context would mean that a player who has successfully put away a few kills in a row would have a higher likelihood of a kill on his or her next spike than the player's long-term kill percentage would suggest. A cold hand would represent the opposite, that a player whose last few spike attempts have resulted in errors (e.g., ball hit out of bounds) would have higher than usual odds of an error on the next attempt than his/her long-term percentages would suggest. Within the constraints of the data set to which the authors had access (partial game-sequence data from top players in a German men's professional league), the analyses were

Comparing Forecasting Models for 2011 Women's NCAA Tourney

Happy New Year! I wanted to close out discussion of the 2011 NCAA women's volleyball tournament by examining the effectiveness of my newly developed Conference-Adjusted Combined Offensive/Defensive (CACOD) ranking system at predicting the outcome of tournament matches. Details of the formula and the full set of CACOD rankings are available here . In short, however, for each team in the NCAA field, the CACOD took the " ratio of its own overall [regular] season hitting percentage (offense) divided by the overall hitting percentage it has allowed the opposition (defense)." This ratio was then multiplied by an adjustment factor based on a team's conference (the stronger the conference, the more the adjustment factor raised the team's ranking). For each of the 63 matches in the tournament, I simply looked at whether the team with the higher CACOD rating won or lost. The CACOD's record is shown below, along with those from other leading rating systems (shown in