The Donaghy Effect
Find +EV NBA Bets
Referee-adjusted spreads (ATS), over/under (O/U), and moneyline - plus player props. Models built to identify positive expected value, not simple hit rates.
Referees Don't Call Games the Same Way
Each point is an NBA referee. Where they fall shows how often foul calls favor home vs away teams, revealing tendencies that shift pace, free throws, and late-game variance.
Data table: referee foul bias snapshot
| Referee | Games | Fouls on home team | Fouls on away team | Bias |
|---|---|---|---|---|
| Josh Tiven | 172 | 6.62 | 6.70 | +0.08 |
| Marc Davis | 172 | 6.10 | 6.06 | -0.04 |
| Tyler Ford | 169 | 7.33 | 7.16 | -0.17 |
| James Williams | 169 | 6.82 | 6.70 | -0.12 |
| Zach Zarba | 165 | 6.84 | 6.60 | -0.24 |
| Mitchell Ervin | 161 | 6.86 | 6.91 | +0.05 |
| Gediminas Petraitis | 161 | 6.51 | 6.47 | -0.04 |
| Tony Brothers | 161 | 8.07 | 7.27 | -0.80 |
| Ray Acosta | 160 | 6.36 | 6.20 | -0.16 |
| Pat Fraher | 160 | 7.01 | 6.92 | -0.09 |
| Mark Lindsay | 159 | 7.42 | 7.79 | +0.37 |
| Kevin Scott | 159 | 6.30 | 6.69 | +0.39 |
| Justin Van Duyne | 155 | 6.71 | 7.14 | +0.43 |
| Jacyn Goble | 155 | 6.05 | 5.72 | -0.33 |
| Nick Buchert | 154 | 7.56 | 7.59 | +0.03 |
| Brian Forte | 152 | 5.88 | 5.62 | -0.26 |
| Curtis Blair | 151 | 6.77 | 6.89 | +0.12 |
| Brent Barnaky | 150 | 6.19 | 6.17 | -0.02 |
| Courtney Kirkland | 150 | 7.14 | 7.15 | +0.01 |
| Scott Foster | 150 | 7.96 | 8.35 | +0.39 |
| Natalie Sago | 148 | 5.86 | 5.66 | -0.20 |
| Sean Wright | 147 | 7.62 | 7.33 | -0.29 |
| Karl Lane | 144 | 6.70 | 6.36 | -0.34 |
| Sean Corbin | 143 | 6.48 | 6.87 | +0.39 |
| Marat Kogut | 140 | 6.80 | 6.71 | -0.09 |
| Kevin Cutler | 138 | 5.76 | 7.30 | +1.54 |
| Andy Nagy | 136 | 7.23 | 7.18 | -0.05 |
| Ed Malloy | 135 | 6.61 | 6.50 | -0.11 |
| Bill Kennedy | 135 | 5.90 | 5.74 | -0.16 |
| Phenizee Ransom | 134 | 6.60 | 6.87 | +0.27 |
| Scott Twardoski | 130 | 6.07 | 6.47 | +0.40 |
| CJ Washington | 129 | 7.05 | 7.09 | +0.04 |
| Eric Dalen | 127 | 6.34 | 6.28 | -0.06 |
| JB DeRosa | 127 | 7.57 | 7.46 | -0.11 |
| John Goble | 127 | 7.51 | 7.17 | -0.34 |
| James Capers | 122 | 6.68 | 7.07 | +0.39 |
| Ben Taylor | 120 | 7.42 | 7.15 | -0.27 |
| Brett Nansel | 118 | 5.87 | 5.47 | -0.40 |
| Jason Goldenberg | 118 | 6.32 | 6.70 | +0.38 |
| Mousa Dagher | 118 | 6.53 | 6.07 | -0.46 |
| David Guthrie | 116 | 6.18 | 5.93 | -0.25 |
| Matt Myers | 113 | 6.49 | 6.57 | +0.08 |
| Nate Green | 112 | 6.60 | 6.62 | +0.02 |
| Evan Scott | 111 | 6.26 | 6.22 | -0.04 |
| Jenna Schroeder | 110 | 5.83 | 6.09 | +0.26 |
| Dannica Mosher-Baroody | 108 | 6.35 | 6.32 | -0.03 |
| Michael Smith | 103 | 7.74 | 7.59 | -0.15 |
| Brandon Schwab | 103 | 6.02 | 6.23 | +0.21 |
| Derrick Collins | 101 | 5.77 | 6.09 | +0.32 |
| Dedric Taylor | 101 | 6.44 | 6.33 | -0.11 |
| Jonathan Sterling | 100 | 6.20 | 6.66 | +0.46 |
| Tre Maddox | 98 | 6.68 | 6.78 | +0.10 |
| Brandon Adair | 98 | 5.12 | 5.61 | +0.49 |
| Danielle Scott | 97 | 5.67 | 5.74 | +0.07 |
| Robert Hussey | 96 | 6.61 | 6.42 | -0.19 |
| Suyash Mehta | 95 | 6.47 | 6.74 | +0.27 |
| Tom Washington | 95 | 5.40 | 5.55 | +0.15 |
| John Conley | 89 | 6.22 | 6.00 | -0.22 |
| Matt Kallio | 89 | 5.36 | 6.07 | +0.71 |
| Sha'Rae Mitchell | 87 | 5.69 | 6.33 | +0.64 |
| Rodney Mott | 87 | 6.59 | 7.07 | +0.48 |
| Aaron Smith | 86 | 5.72 | 5.91 | +0.19 |
| Derek Richardson | 79 | 5.78 | 6.23 | +0.45 |
| Che Flores | 79 | 5.95 | 6.24 | +0.29 |
| Intae Hwang | 76 | 4.79 | 5.18 | +0.39 |
| John Butler | 75 | 6.27 | 6.29 | +0.02 |
| Matt Boland | 71 | 6.42 | 5.39 | -1.03 |
| Ashley Moyer-Gleich | 70 | 5.31 | 5.57 | +0.26 |
| J.T. Orr | 65 | 6.88 | 6.42 | -0.46 |
| Simone Jelks | 61 | 6.03 | 5.23 | -0.80 |
| JD Ralls | 58 | 6.95 | 7.48 | +0.53 |
| Tyler Ricks | 48 | 6.35 | 5.92 | -0.43 |
| Leon Wood | 44 | 6.30 | 6.64 | +0.34 |
| Scott Wall | 37 | 7.59 | 7.30 | -0.29 |
| Pat O'Connell | 27 | 7.41 | 7.19 | -0.22 |
| Jenna Reneau | 26 | 5.85 | 5.73 | -0.12 |
| Brent Haskill | 21 | 6.90 | 5.71 | -1.19 |
| Biniam Maru | 18 | 6.44 | 5.50 | -0.94 |
| Lauren Holtkamp | 15 | 6.80 | 6.33 | -0.47 |
| Tyler Mirkovich | 11 | 5.82 | 6.73 | +0.91 |
Bias = away fouls minus home fouls (positive favors home).
Game Analysis & Referee Impact
Why referee bias matters for ATS and totals
Referees shape pace, foul rate, and free throw volume, which can move totals and ATS outcomes even when teams are unchanged.
Read more about referee impact
Referee tendencies play an important but often misunderstood role in NBA betting markets. While officiating does not determine outcomes, different crews consistently vary in how tightly games are called and how physical play is allowed.
The impact is strongest at the game environment level, not the individual player level. Fouls, stoppages, and free throws accumulate across the full game, shaping scoring distributions and margins in ways that are difficult for sportsbooks to fully price nightly.
These effects show up most in totals and ATS markets, where pace, foul rate, and late-game whistles can swing whether a favorite pulls away or an underdog stays within the number.
Referee data is not used to predict individual player props. Player performance is driven by usage, minutes, role, matchup, and team context. We apply referee context where it is most reliable: modeling game-level scoring environments and spread dynamics on our NBA referee trends pages.
ATS impact: Which referees see favorites covering vs dogs covering.
Over/Under bias: Officials whose games consistently land above or below the total.
Team-specific officiating: How a selected team is whistled by each crew across seasons.
Raptors
@
Pacers
Referee Crew Impact
Data table: referee crew tendencies
| Referee | O/U record | Over rate |
|---|---|---|
| Brian Forte | 70-91 | 43.5% |
| Nate Green | 55-64 | 46.2% |
| Jenna Reneau | 15-18 | 45.5% |
Player Prop Analysis
How Our Player Prop Analysis Works
We evaluate props with probability distributions that compare historical lines to current form, helping surface when a market number is mispriced.
Read more about player prop methodology
vs Historical Line
We compare a player's past results to the actual Vegas line set for each specific game. Because those historical lines already accounted for opponent strength, injuries, pace, and matchup context, this analysis provides a context-adjusted view of how the player performed relative to market expectations.
vs Current Line
We also model the player's recent production directly against today's line. This isolates current trends in form, usage, minutes, and role, independent of opponent adjustments.
By combining both perspectives, our system highlights when today's line may be mispriced and whether value exists on the over or under.
Understanding Key Metrics
- HIT Rate:
- The percentage of times a player went over (or under) the betting line in recent games.
- MODEL Probability:
- Our statistical prediction using historical performance, matchup context, referee tendencies, and variance analysis.
- EV (Expected Value):
- The mathematical edge on a bet expressed as a percentage. Positive EV means the bet is profitable long-term. EV = (probability × win_payout) - ((1-probability) × bet_amount)
Brandon Ingram
Raptors @ Pacers
*Stats and charts do not include overtime games because they inflate statistics and predictions
| Date | Opponent | H/A | Actual | Line | Vs line | Hit |
|---|---|---|---|---|---|---|
| Dec 21 | BOS | HOME | 24.0 | 23.5 | +0.5 | ✓ |
| Dec 21 | BKN | AWAY | 19.0 | 22.5 | -3.5 | ✗ |
| Dec 24 | MIA | AWAY | 12.0 | 23.5 | -11.5 | ✗ |
| Dec 27 | WAS | AWAY | 29.0 | 23.5 | +5.5 | ✓ |
| Dec 30 | ORL | HOME | 17.0 | 24.5 | -7.5 | ✗ |
| Jan 01 | DEN | HOME | 30.0 | 23.5 | +6.5 | ✓ |
| Jan 04 | ATL | HOME | 29.0 | 23.5 | +5.5 | ✓ |
| Jan 06 | ATL | HOME | 19.0 | 22.5 | -3.5 | ✗ |
| Jan 08 | CHA | AWAY | 6.0 | 21.5 | -15.5 | ✗ |
| Jan 13 | PHI | HOME | 17.0 | 21.5 | -4.5 | ✗ |
Expected Value Summary
vs Historical Lines
| Prop | O/U | 5G | 10G | 20G | H2H |
|---|---|---|---|---|---|
| Points | 23.5 | U 3.3 | U 7.1 | O 5.5 | O 47.5 |
| Rebounds | 6.5 | O 43.5 | O 28.8 | O 28.1 | O 71.8 |
| Assists | 3.5 | U 11.4 | U 8.0 | U 1.6 | U 22.1 |
| 3-Pointers | 1.5 | U 8.1 | U 12.8 | U 6.3 | U 56.0 |
| Double Double | +550 | Y 49.8 | 11.1 | 49.8 | 79.0 |
■ Green indicates positive expected value | ■ Red indicates negative expected value
vs Current Line
| Prop | O/U | 5G | 10G | 20G | H2H |
|---|---|---|---|---|---|
| Points | 23.5 | U 0.1 | U 9.0 | O 4.5 | O 37.9 |
| Rebounds | 6.5 | O 33.5 | O 15.6 | O 8.8 | O 6.3 |
| Assists | 3.5 | U 11.4 | U 5.2 | U 0.5 | O 10.6 |
| 3-Pointers | 1.5 | U 8.1 | U 0.8 | O 1.7 | U 35.9 |
| Double Double | +550 | 49.8 | 11.1 | 49.8 | 79.0 |
Today's Highest +EV Props
Top points props from the last 10 games using historical lines. Not picks - just model-ranked value.
| Player | Prop | Line | Model % | EV |
|---|---|---|---|---|
| Sam Merrill | Points O | 12.5 | 76.1% | +53.7% |
| Miles McBride | Points O | 10.5 | 75.5% | +42.9% |
| Terance Mann | Points U | 6.5 | 74.4% | +42.0% |
| Peyton Watson | Points O | 19.5 | 71.5% | +40.2% |
| Christian Braun | Points U | 9.5 | 76.9% | +37.0% |
About The Donaghy Effect
We model props, spreads, and totals with referee-adjusted probability distributions to surface +EV opportunities.
Read more about how it works
Our models use historical data with referee-specific context to generate probability distributions for each prop. Rather than relying on simple averages or hit rates, we account for variance in player and team performance, matchup dynamics, and officiating crew tendencies.
Tools & Features
- NBA Player Props Tool - Probability distributions, hit rates, and fair lines for points, rebounds, assists, threes, and more.
- NBA Matchups (ATS & Over/Under) - Spread and total projections with uncertainty bands and referee impact.
- +EV Props Finder - Automatically ranks positive expected value opportunities by confidence level.
- +EV Matchups - Daily edges on spreads and totals ranked by expected value.
Whether you're analyzing player props, looking for ATS value, or hunting for totals edges, the platform keeps the focus on probabilities and EV instead of streak chasing.
Pricing
Compare plans to unlock full +EV boards, filters, and referee analysis.
Frequently Asked Questions
What does positive expected value (+EV) mean?
We compare the model probability to implied odds and compute the expected return. Positive EV signals a long-run edge, not a guarantee on any single bet.
Do you cover ATS and totals?
Yes. Matchup pages show ATS and over/under probabilities with referee-adjusted distributions and team context.
What makes your player prop models different?
We use probability distributions and historical line context instead of simple hit rates, which helps surface mispriced lines.
How do you handle referee impact?
We model foul rate, pace, and bias at the game level and fold it into totals and ATS. We do not use referee data to claim player-prop causality.
How often is the data updated?
Daily, and refreshed as lines and referee assignments change.
Is this betting advice?
No. The Donaghy Effect provides tools and data only; all decisions and risk remain with the user.
Join The Donaghy Effect
Subscribe now and lock in 30% off forever - early subscriber pricing ends at launch