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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 Thu, 11 Jun 2026 12:10:01 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.

1
Thinking Basketball YT
n = 182
Lift: +8.01
Lift: +8.01 | 95% CI [+9.09, +14.47] | n=182
2
Portland Trail Blazers (Official) YT fan
n = 107
Lift: +3.08
Lift: +3.08 | 95% CI [+2.03, +5.08] | n=107
3
JxmyHighroller YT
n = 138
Lift: +2.14
Lift: +2.14 | 95% CI [+0.37, +8.25] | n=138
#4
The Bill Simmons Podcast Pod
n = 226
Lift: -0.73
Lift: -0.73 | 95% CI [-2.57, +0.82] | n=226
#5
Pro Football Focus news_rss analyst
n = 2 · small sample
Lift: -10.76
Lift: -10.76 | 95% CI [-23.90, -16.55] | n=2
NBA

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 POD The Bill Simmons Podcast 222 +3477
2 YT JxmyHighroller 59 +3439
3 YT Portland Trail Blazers (Official) 54 +3523
4 YT Thinking Basketball 37 +2746
5 YT Mike Korzemba 34 -1052
6 YT ESPN NBA 30 +1176
7 YT All The Smoke (Barnes/Jackson) 27 +2062
8 YT Bleacher Report 15 +372
9 NEW CBS Sports NFL 15 +265
10 YT The Athletic NFL Show 13 +850
11 YT HoopsHype 12 +1053
12 YT CBS Sports NFL 8 +410
13 NEW ESPN NFL News 4 +225
14 YT The Pat McAfee Show (YouTube) 2 +143
15 YT Chris Mannix (Sports Illustrated) 2 +143
16 NEW Pro Football Focus 2 +86
17 NEW Acme Packing Company (GB) 1 +45
18 NEW Windy City Gridiron (CHI) 1 +42

Explicit-pick hit rate

Live

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
The Bill Simmons Podcast Pod
n = 47
Hit rate: 63.9%
Hit rate: 63.9% | 95% CI [65.1%, 88.0%] | n=47
2
Thinking Basketball YT
n = 22
Hit rate: 59.7%
Hit rate: 59.7% | 95% CI [61.5%, 92.7%] | n=22
3
JxmyHighroller YT
n = 11
Hit rate: 55.7%
Hit rate: 55.7% | 95% CI [52.3%, 94.9%] | n=11
#4
Portland Trail Blazers (Official) YT fan
n = 20
Hit rate: 55.7%
Hit rate: 55.7% | 95% CI [48.1%, 85.5%] | n=20
#5
CBS Sports NFL news_rss network
n = 1 · small sample
Hit rate: 49.0%
Hit rate: 49.0% | 95% CI [0.0%, 79.3%] | n=1

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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