A Structured System for Smarter Sports Analysis

Quantitative models. Transparent record. Risk-managed position recommendations — posted before tip-off, logged forever.

All-Time Units

+79.5u

All-Time Record

761–616–23

All-Time Win Rate

54.4%

✓ 761–616 all-time record  ·  54.4% all-time win rate on Rage Picks  ·  All picks timestamped before game time

TENNIS - ATP Today, Today, 5:05 PT
FREE PICK

Nakashima @ Shelton

Ben Shelton

Spread -2.5

Opening
-230.0
Current
-213.0
17.0 pts
Odds
+102
Market 49.5% Win probability
Rage Model 73.1% Win probability
Model Edge +23.6% +EV differential
Conviction Score 3.5 / 5
1.5 — Lean3 — Standard Play5 — Max Allocation

Why This Has Edge

  • Our Rage Model projects a 73.1% win probability for Ben Shelton covering -2.5 games, delivering a massive +20.0% EV differential over the market-implied 49.5% probability at +102 odds.
  • Shelton holds a dominant 5-0 career head-to-head record against Nakashima, backed by an elite serve profile (77%+ first-serve points won H2H) that consistently keeps Nakashima on defense and enables multi-game margin breaks.
Free Picks ’26 100-80
Win Rate 54.3%
All Bets ’26 761-616
Units +79.5u

Today’s Full Board Includes

  • 7 additional edges on today’s slate
  • Conviction scores & model win probabilities
  • Market movement tracking
Try 3 Days for FREE

Full slate & edge report · Cancel anytime

Recent Results

Date Game Pick Market Odds Margin Tier Result
2026-08-12 WNBA · Chicago Sky @ Golden State Valkyries Chicago Sky +9.5 Spread -105 -10.5 B ✗ Loss
2026-08-12 TENNIS - WTA · Elena Rybakina @ Coco Gauff Coco Gauff Moneyline -132 -1.0 B ✗ Loss
2026-08-12 WNBA · Toronto Tempo @ Dallas Wings Dallas Wings -8.5 Spread -115 -2.5 B ✗ Loss
2026-08-12 PARLAY · Parlay Rafael Jodar (h2h) + Ben Shelton (h2h) PARLAY +126 -0.5 A ✗ Loss
2026-08-12 TENNIS - ATP · Brandon Nakashima @ Rafael Jodar Rafael Jodar -3.5 Spread +104 -5.5 B+ ✗ Loss
2026-08-12 MLB · Minnesota Twins @ Baltimore Orioles Minnesota Twins Moneyline -104 +2.0 B ✓ Win
2026-08-11 MLB · Kansas City Royals @ Los Angeles Dodgers Los Angeles Dodgers -1.5 Spread -120 -0.5 B ✗ Loss

⚙️ Not Picks. A System.

Most sports fans lose because they act on gut instinct. RagePicks runs quantitative models across lines, totals, and game winners — identifying statistically significant +EV opportunities before the market closes. Every recommendation is timestamped, every result recorded. No narrative, no deletes, no excuses.

FAQ

Is this free?

Yes — one free pick per day, always. Subscribe for the full slate.

What is +EV analysis?

+EV analysis (positive expected value) means the probability of winning exceeds what the odds imply. RagePicks models identify lines where the market is mispriced — giving you a mathematical edge before the market corrects. Consistent +EV analysis is the only long-term profitable approach. Learn how our models work →

How does the quantitative model identify edges?

Our models analyze lines, totals, and game winners across major sports markets, incorporating historical trends, market movement tracking, and contextual game data to identify statistically significant +EV opportunities before the market closes. Dive deeper into our methodology →

Are losses ever deleted?

Never. Every result — wins, losses, and pushes — is permanently recorded. Full pick history is public on the history page and the performance dashboard for per sport filtering result margins.

What sports do you cover?

NBA, WNBA, NFL, NCAAB, NCAAF, NHL, MLB, UFC, BOXING, SOCCER (WP, MLS), and TENNIS (ATP, WTA)

What does R.A.G.E. stand for?

Recursive — a high-level, adaptive AI architecture that continuously refines its own outputs.
Algorithm — no gut feelings, no human bias — pure, cold math from start to finish.
Generating — actively producing actionable value out of raw, chaotic sports data every day.
Edge — the holy grail of sports betting: a statistically significant +EV differential over the market.

Most bettors rage at bad beats. We turned that word into a clinical, automated system designed to beat the books. See how the model works →