Iowa
2019 Team Stats (23 games)
78.7
PPG
74.9
Opp
+3.8
Margin
46.6%
FG%
38.3%
3P%
72.6%
FT%
33.6
RPG
15.9
APG
11.6
TO
-
Est. poss./game
81.6
Adj PPG
69.8
Adj Opp PPG
#35
Scoring Rank
Model Outputs
2018-2019
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 โ | Data withheld: source-integrity hold | Historical Elo fit predates the season cutoff fix and has no verified season-bounded input receipt. |
| Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More โ | 1214 (#40) | - |
| Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. More โ | 1038 (#112) | HCA +83 elo |
| Margin Margin Linear team-strength model fit on point differential instead of binary wins. More โ | +13.1 (#35) | HCA +2.8 |
| Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. More โ | +10.5 (#45) | HCA +2.7 |
| Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More โ | 0.661 (#77) | - |
| Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. More โ | 0.892 (#40) | AdjNet +18.3 |
| Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. More โ | 0.895 (#40) | AdjNet +18.6 |
| Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. More โ | 0.746 (#35) | AdjO 81.6 | AdjD 69.8 |
| Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. More โ | 0.604 (#66) | AdjO 76.5 | AdjD 71.9 |
| Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. More โ | 0.767 (#45) | 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 โ | 0.743 (#49) | Blend of Elo, BT, Margin, PythLog, PtsOD |
| Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. More โ | 1212 (#43) | RD 105 | GP 35 |
2019 Schedule & Results
2019 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 GP | PM/G | PM/40 | FG% | 3P% | FT% | RAPM | TS% | eFG% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tyler Cook
|
- | 33 | 30.8 | 14.5 | 7.6 | 2.4 | 0.7 | 0.5 | 2.8 | 8.2 | 14.8 | 78 | 28 | 2.8 | 3.8 | 47.6 | 0.0 | 64.1 | 0.58 | 70.4 | 47.6 |
Luka Garza
|
- | 32 | 23.7 | 13.1 | 4.5 | 0.9 | 0.3 | 0.5 | 1.3 | 6.5 | 11.5 | 105 | 27 | 3.9 | 6.9 | 54.8 | 34.0 | 81.4 | 4.48 | 87.7 | 58.9 |
Jordan Bohannon
|
G | 35 | 31.0 | 11.6 | 2.4 | 3.4 | 0.7 | 0.0 | 1.7 | 6.5 | 9.9 | 83 | 30 | 2.8 | 4.0 | 39.2 | 39.6 | 86.2 | 0.47 | 76.3 | 53.1 |
Joe Wieskamp
|
- | 35 | 27.7 | 11.1 | 4.9 | 1.1 | 0.9 | 0.5 | 1.1 | 5.7 | 11.6 | 67 | 30 | 2.2 | 3.6 | 50.7 | 45.1 | 78.5 | 0.49 | 82.3 | 63.4 |
Isaiah Moss
|
G | 35 | 24.2 | 9.2 | 2.9 | 1.8 | 0.9 | 0.2 | 1.4 | 5.9 | 7.6 | 63 | 30 | 2.1 | 3.8 | 41.1 | 43.7 | 79.2 | -0.79 | 69.7 | 50.2 |
Nicholas Baer
|
F | 35 | 19.2 | 6.7 | 4.6 | 1.3 | 1.0 | 1.0 | 0.7 | 4.0 | 9.9 | 77 | 30 | 2.6 | 5.8 | 40.7 | 34.4 | 67.4 | 2.59 | 74.3 | 52.1 |
Ryan Kriener
|
F | 35 | 13.8 | 5.7 | 3.0 | 0.7 | 0.4 | 0.3 | 1.0 | 3.2 | 5.9 | 62 | 30 | 2.1 | 6.4 | 56.6 | 34.8 | 69.0 | 3.56 | 75.3 | 60.2 |
Connor McCaffery
|
- | 34 | 18.6 | 4.4 | 1.4 | 3.0 | 0.7 | 0.1 | 1.3 | 1.9 | 6.5 | 11 | 29 | 0.4 | 0.9 | 28.6 | 22.7 | 69.4 | -1.9 | 82.5 | 32.5 |
Cordell Pemsl
|
F | 2 | 15.0 | 4.0 | 4.0 | 1.0 | 0.0 | 0.5 | 2.5 | 0.0 | 7.0 | - | - | - | - | 0 | 0 | 0 | -0.34 | 0 | 0 |
Maishe Dailey
|
G | 35 | 12.4 | 2.5 | 1.5 | 0.9 | 0.3 | 0.0 | 0.6 | 2.0 | 2.5 | 55 | 30 | 1.8 | 6.2 | 35.7 | 20.0 | 72.2 | 0.06 | 55.8 | 41.4 |
Austin Ash
|
- | 9 | 4.3 | 1.7 | 0.6 | 0.4 | 0.3 | 0.0 | 0.0 | 1.9 | 1.1 | -9 | 8 | -1.1 | -10.7 | 23.5 | 26.7 | 0 | -4.17 | 44.1 | 35.3 |
Riley Till
|
F | 16 | 4.6 | 1.4 | 0.6 | 0.3 | 0.3 | 0.1 | 0.2 | 0.8 | 1.8 | 6 | 14 | 0.4 | 3.8 | 50.0 | 0.0 | 66.7 | -1.62 | 59.1 | 50.0 |
Nicolas Hobbs
|
G | 6 | 2.3 | 1.3 | 0.3 | 0.5 | 0.0 | 0.0 | 0.2 | 0.7 | 1.3 | -12 | 6 | -2.0 | -38.2 | 75.0 | 66.7 | 0 | -3.41 | 100.0 | 100.0 |
Michael Baer
|
- | 9 | 2.2 | 0.7 | 0.4 | 0.0 | 0.0 | 0.0 | 0.1 | 0.4 | 0.6 | -15 | 9 | -1.7 | -35.0 | 50.0 | 100.0 | 25.0 | -4.23 | 52.1 | 62.5 |
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