UC Irvine
2022 Team Stats (2 games)
65.0
PPG
61.1
Opp
+5.4
Margin
39.8%
FG%
34.4%
3P%
70.8%
FT%
31.0
RPG
9.5
APG
10.0
TO
79.3
Pace
Model Outputs
2021-2022
Output is shown as model rating with league rank in parentheses when available.
| Model | Output | Notes |
|---|---|---|
| Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → | 1314 (#69) | - |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → | 1292 (#101) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 1035 (#126) | HCA +115 elo |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More → | 991 (#441) | HCA +115 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More → | +28.7 (#104) | HCA +2.7 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | +10.2 (#203) | HCA +2.4 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More → | -10.4 (#476) | HCA +2.4 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → | 0.710 (#19) | - |
| Efficiency Efficiency Tempo-adjusted efficiency version of Pythagorean ratings. More → | 0.236 (#204) | NetEff -10.1 |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More → | 0.393 (#78) | AdjNet -3.8 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More → | 0.393 (#78) | AdjNet -3.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.566 (#165) | AdjO 66.1 | AdjD 63.2 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More → | 0.442 (#485) | AdjO 68.1 | AdjD 70.7 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.729 (#173) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More → | 0.321 (#451) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.696 (#173) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. More → | 0.340 (#450) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 1060 (#178) | RD 139 | GP 23 |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More → | 866 (#545) | RD 350 | GP 2 |
2022 Schedule & Results
2022 Roster
Minutes by Position
The surface stays filled across the five on-court roles. Use the labels or legend to isolate how each player absorbs guard-to-big minutes.
| Player | Pos | GP | MIN | PTS | REB | AST | STL | BLK | TO | FGA | Numbers | PM | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C. Welp
|
F | 2 | 31.0 | 20.5 | 4.5 | 0.5 | 0.5 | 0.5 | 0.5 | 21.5 | 4.5 | 46 | 1.8 | 2.7 | 39.5 | 22.2 | 100.0 | 0.41 | 45.4 | 41.9 |
DJ Davis
|
- | 2 | 26.0 | 16.5 | 1.5 | 1.5 | 1.5 | 0.0 | 1.5 | 10.5 | 9.0 | 48 | 2.0 | 3.6 | 57.1 | 54.5 | 100.0 | 0.75 | 73.9 | 71.4 |
D. Baker
|
- | 18 | 26.6 | 11.3 | 2.2 | 1.2 | 0.7 | 0.2 | 1.8 | 1.8 | 11.9 | 93 | 4.9 | 7.6 | 46.9 | 66.7 | 66.7 | 0.91 | 293.0 | 56.2 |
Justin Hohn
|
- | 23 | 22.7 | 8.1 | 2.9 | 2.0 | 1.1 | 0.2 | 1.7 | 1.0 | 11.6 | 59 | 2.4 | 4.1 | 59.1 | 50.0 | 100.0 | 0.66 | 406.5 | 68.2 |
A. Johnson
|
F | 2 | 28.0 | 6.5 | 7.0 | 1.0 | 0.0 | 3.5 | 0.5 | 5.0 | 12.5 | 24 | 1.0 | 1.8 | 50.0 | 0 | 75.0 | 0.25 | 55.3 | 50.0 |
JC Butler
|
- | 23 | 21.7 | 6.3 | 2.6 | 1.0 | 0.6 | 0.0 | 1.1 | 0.7 | 8.8 | -14 | -0.6 | -1.0 | 33.3 | 0.0 | 75.0 | 0.31 | 394.2 | 33.3 |
Bent Leuchten
|
- | 22 | 7.1 | 3.5 | 1.9 | 0.3 | 0.1 | 0.2 | 0.7 | 0.7 | 4.6 | 73 | 3.0 | 17.5 | 53.3 | 50.0 | 75.0 | 0.69 | 226.7 | 56.7 |
A. Henry
|
- | 14 | 8.8 | 2.2 | 1.4 | 0.4 | 0.4 | 0.1 | 0.5 | 0.2 | 3.7 | 33 | 2.4 | 10.2 | 33.3 | 0.0 | 75.0 | 0.35 | 325.6 | 33.3 |
Ofure Ujadughele
|
- | 23 | 12.4 | 2.1 | 2.8 | 1.1 | 0.8 | 0.0 | 1.0 | 0.7 | 5.2 | 64 | 2.6 | 8.3 | 40.0 | 0.0 | 0.0 | 0.68 | 142.4 | 40.0 |
Dean Keeler
|
- | 11 | 7.5 | 1.9 | 2.2 | 0.4 | 0.3 | 0.3 | 0.6 | 0.5 | 3.9 | 48 | 4.0 | 19.7 | 40.0 | 0 | 0 | 0.55 | 210.0 | 40.0 |
I. Lee
|
G | 2 | 16.0 | 1.0 | 1.5 | 1.0 | 0.0 | 0.0 | 0.5 | 2.5 | 0.5 | 95 | 3.8 | 8.7 | 20.0 | 0.0 | 0 | 1.27 | 20.0 | 20.0 |
E. Tshimanga
|
F | 2 | 7.5 | 1.0 | 3.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.5 | 1.5 | 22 | 0.9 | 3.0 | 33.3 | 0 | 0.0 | 0.28 | 21.0 | 33.3 |
L. Redfield
|
- | 20 | 8.6 | 0.9 | 0.8 | 0.6 | 0.3 | 0.0 | 0.4 | 0.1 | 2.1 | 28 | 1.3 | 5.5 | 50.0 | 0 | 0 | 0.1 | 475.0 | 50.0 |
D. Tillis
|
F | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
A. McBirney-Griffin
|
F | 0 | - | 0.0 | 0.0 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
Numbers/Game vs RAPM
X-axis = Numbers/Game (PTS+REB+AST+STL+BLK-TO-FGA), Y-axis = RAPM.
Advanced: Numbers = PTS+REB+AST+STL+BLK-TO-FGA, PM = total +/-, PM/G = per game, PM/40 = per 40 minutes, RAPM = Regularized Adj Plus-Minus, TS% = True Shooting, eFG% = Effective FG