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Beta · Phase 1 Anti-Tout Science Desk

Trout Tout Track real tout ROI vs our public model picks.

Per-source pick-accuracy leaderboard. Sample-size shrunk (Bayesian), 95% confidence intervals. Negative lift means the source's calls have underperformed the sport-position baseline — that's a trout. Fade it.

Last updated Wed, 10 Jun 2026 13:51:47 GMT · refreshes every 6h

Sentiment-as-pick lift

How much better (or worse) than baseline did players score after this source called them out? Sign-weighted: bullish takes "win" when the player exceeds baseline; bearish takes "win" when they underperform. Higher = better.

No sentiment-lift data for this filter.
NFL

Sources making the boldest player calls (high-confidence + strong sentiment, last 90d). Hot-take index sums signed conviction; red = bullish-loaded, green = contrarian/bearish.

1 NEW Pro Football Focus 309 +19434
2 NEW ESPN NFL News 171 +8684
3 NEW CBS Sports NFL 163 +4117
4 YT Establish The Run 100 +4892
5 NEW Yahoo Sports NFL 95 +3620
6 YT JT O'Sullivan / The QB School 87 +4596
7 NEW ProFootballTalk 84 +2906
8 NEW Pro Football Rumors 72 +1549
9 YT NFL Network 71 +5056
10 YT FantasyPros 61 +2959
11 NEW SB Nation NFL 46 +1506
12 NEW Buffalo Rumblings (BUF) 42 +2000
13 NEW Arrowhead Pride (KC) 37 +1858
14 YT Voch Lombardi 36 +2112
15 POD The Bill Simmons Podcast 28 +555
16 YT Up & Adams (Kay Adams) 26 +1815
17 YT Chris Mannix (Sports Illustrated) 26 +1530
18 YT Mina Kimes Show 26 +1365
19 NEW Niners Nation (SF) 24 +1142
20 NEW Bucs Nation (TB) 21 +1274

Explicit-pick hit rate

NFL pending

When a source makes an explicit over/under call, how often does it settle in their favor? Each pick matched to the closest pickem line + graded against the actual box score. NBA fully wired; NFL settlement bridge ships with Phase 2 (live on Sunday).

1
Pro Football Rumors news_rss analyst
n = 9 · small sample
Hit rate: 80.0%
Hit rate: 80.0% | 95% CI [61.0%, 100.0%] | n=9
2
Yahoo Sports NFL news_rss network
n = 3 · small sample
Hit rate: 66.7%
Hit rate: 66.7% | 95% CI [34.2%, 100.0%] | n=3
3
Big Blue View (NYG) news_rss fan
n = 2 · small sample
Hit rate: 66.7%
Hit rate: 66.7% | 95% CI [34.2%, 100.0%] | n=2
#4
Niners Nation (SF) news_rss fan
n = 1 · small sample
Hit rate: 60.0%
Hit rate: 60.0% | 95% CI [20.6%, 100.0%] | n=1
#5
Arrowhead Pride (KC) news_rss fan
n = 2 · small sample
Hit rate: 50.0%
Hit rate: 50.0% | 95% CI [9.4%, 90.5%] | n=2
#6
ESPN NFL News news_rss network
n = 9 · small sample
Hit rate: 42.9%
Hit rate: 42.9% | 95% CI [6.2%, 79.2%] | n=9
#7
CBS Sports NFL news_rss network
n = 25
Hit rate: 40.0%
Hit rate: 40.0% | 95% CI [15.2%, 64.6%] | n=25
#8
Windy City Gridiron (CHI) news_rss fan
n = 6 · small sample
Hit rate: 40.0%
Hit rate: 40.0% | 95% CI [0.0%, 79.3%] | n=6
#9
SB Nation NFL news_rss fan
n = 2 · small sample
Hit rate: 33.3%
Hit rate: 33.3% | 95% CI [0.0%, 65.8%] | n=2

What the columns mean

Lift
Shrunk point estimate of how much better (or worse) the source's calls performed vs the sport-position baseline. Positive = the source helped, negative = trout.
Hit rate
Win % on explicit picks, excluding pushes. 52.4% is breakeven vs -110 juice.
95% CI
Confidence interval around the point estimate. Wide bars = thin sample; thin bars crossing 0 (or 50%) = the source is statistically indistinguishable from noise.
n
Sample size — observations contributing to the estimate. n < 10 is flagged as a small sample (faded CI bar).
ROI
Return on investment if you'd faded (or followed) the source at -110 unit-flat. Reported in the per-source detail page.
CLV
Closing-line value — how much the line moved after the source's pick, in the source's direction (avg %). The strongest long-run skill signal.

Methodology. Lift is computed over a 14-day window after each episode published. Shrinkage uses an empirical-Bayes prior with pseudo-count 50 toward neutrality (lift = 0 or hit-rate = 50%). 95% CIs are Wald (lift) and Wilson (hit rate). Small samples (n < 10) appear but with faded CI bars.

Not investment / betting advice. Historical accuracy doesn't predict future performance, especially with thin samples. Use this as one signal among many.

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