Methodology & glossary
How the numbers work
College Volleyball rates every NCAA Division I women's volleyball team from rally-level play-by-play. This page defines each stat on the site, shows how RallyIQ and win probability are computed, and lists where the data comes from and where it falls short.
Glossary
- Sideout %
The share of rallies a team wins when the opponent is serving. Winning a rally on the opponent’s serve is a sideout, and it earns the serve back.
Sideout % = rallies won while receiving ÷ rallies received
Example: A team that receives 60 serves and wins 38 of those rallies has a 63.3% sideout rate. D1 teams typically sit around 58–62%.
- Break % (point-scoring %)
The share of rallies a team wins on its own serve. Because sideouts are the norm, breaks are what decide sets.
Break % = rallies won while serving ÷ rallies served
Example: Win 22 of 55 service rallies and the break rate is 40.0%.
- Hitting % (attack efficiency)
Kills minus attack errors, divided by total attack attempts. It is shown as a three-digit decimal.
Hit % = (K − E) ÷ TA
Example: 15 kills, 5 errors and 40 attempts gives (15 − 5) ÷ 40 = .250.
- RallyIQ
Our team rating, built from every rally in the NCAA play-by-play. Each team gets a serving strength S and a receiving strength R, fitted together so that P(server wins the rally) = logistic(μ + S_server − R_receiver + h·home). The published number is expected points won per 100 rallies (50 served, 50 received) above an average D1 team on a neutral floor.
Penalized (ridge) maximum likelihood over all serving blocks in PBP-verified final games, shrunk toward a preseason prior
Example: A RallyIQ of +10 means about 10 more points per 100 rallies than an average D1 team would win in the same spot.
- Adjusted break % and adjusted sideout %
The break and sideout rates a team would post against a perfectly average D1 opponent on a neutral floor, read straight off its RallyIQ serving and receiving strengths. They remove schedule strength from the raw rates.
- AdjHit O / AdjHit D
Opponent-adjusted hitting %. AdjHit O is the hitting % a team would post against an average D1 defense; AdjHit D is the hitting % an average D1 offense would post against it (lower is better). They come from a one-pass opponent adjustment on box scores and describe a team; they do not feed the forecasts.
- Win vs avg
Match win probability against an average D1 team on a neutral floor. We run the two rally rates through an exact set-and-match model: every score state to 25, win by two, 15 in set five, best of five. Small rally edges compound over a match.
- Win probability (game pages)
The chance each team wins the match from the current score, serve and set, from the same exact rally-scoring model using the two teams’ RallyIQ strengths. It updates point by point during live matches. It is drawn only when the play-by-play reproduces the official set scores.
- SOS (strength of schedule)
The average RallyIQ of the opponents in the games used for a team’s rating, with its rank among D1 teams. We do not publish a separate strength-of-record metric; for a résumé view, compare the record with SOS, or use the AVCA poll on the Rankings page.
- Four Factors percentiles
On team pages, each stat in four groups (attack, serve, ball control, defense/block) is shown as a percentile against every D1 team this season, so 90 means better than 90% of D1.
- Rotation splits
Sideout and break % in each of the six rotation slots. Slot 1 is the rotation a team starts the set in; each sideout rotates it one spot, so the slot is exact for sets with clean play-by-play. The play-by-play does not say who is serving, so slots are positions in the order, not named servers, and a coach who changes the starting lineup shifts what slot 1 means.
The RallyIQ model
Every final match whose play-by-play reproduces the official set scores contributes two observations: how many rallies the home team served and won, and the same for the away team, each against the other team as receiver. One penalized maximum-likelihood fit (a binomial model with a ridge penalty) solves for every team's serving and receiving strength at once, plus a home-court edge. There is no rating-on-rating iteration: the fit converges to a single optimum in a few Newton steps.
Early in the season the penalty pulls each team toward a preseason rating: last season's final RallyIQ, regressed 10% toward the middle and adjusted by returning production (the share of last year's points scored by players back on the same roster). The pull is worth a fixed number of rallies, so it fades as games accumulate.
The penalty strength, the regression and a final calibration scale were chosen by a forward-chaining backtest on the 2026 season and checked out of sample on the full 2025 season. Recency weighting, discounting blowout rallies and a neutral-site correction were tested and dropped because none improved forecasts in both seasons. The live backtest table is on the Ratings page.
Data sources
- Scores, schedules, box scores and play-by-play: public NCAA game data, refreshed every five minutes during the season.
- Rankings: the AVCA Coaches Poll rank attached to each match.
- Rosters, heights, hometowns and headshots: official school athletics rosters; photos credit and link to the school.
- Everything else (ratings, splits, percentiles, win probability) is computed here from that data.
Caveats
- Some matches have no play-by-play, or a feed that contradicts the official result. Those matches still count in records and box-score stats but are left out of RallyIQ, rotation splits and win-probability charts.
- Scorers correct box scores after matches; we re-pull each final once to pick up corrections.
- Rally splits match play-by-play names to the box score and can miss a few rallies.
- Ratings describe the season so far. Early-season numbers lean on the preseason prior.
- College Volleyball is independent and not affiliated with the NCAA, the AVCA or any school.