Quarterback Rushing Yards Scenario Lab

Independent worksheet • stored Bo Nix / Kansas City context is unverified

Illustrative Inputs No Verified Market

Example Inputs

Assumed Scramble Runs 4
Illustrative count; no verified event or matchup forecast
Yards Per Scramble 5.2 yds
Authored value; not verified historical scramble efficiency
Designed QB Runs / Zone Reads 1
Assumed count; each contributes a fixed 3.7 example yards
Example Sack-Rate Input 7.4%
Arbitrary adjustment: +0.15 yards per percentage-point increase
Example Yardage Threshold 18.0 yds

Illustrative Mean & Normal Tail

Over 18 Yards Favored
Modeled Mean Yardage
24.5
+6.5 example yards relative to threshold
Modeled Upper-Tail Share
71.4%
Favorable Matchup
The percentage is an output of chosen coefficients and a normal curve, not a demonstrated chance of a real Bo Nix result.
Context:Undated Bo Nix / Kansas City fixture; event not verified
Data:No fitted sample, validation, current odds, or market rules
Provenance:Inherited social-post/analyst/partnership claim unverified
Model Readouts — Not Live Telemetry
Model Band: Over 18 Yards Favored
Tail Tier: Favorable Matchup
Mean Yardage: 24.5 yds
Model Tail: 71.4%

A precise percentage still needs evidence

What assumptions create this rushing-yard curve?

The worksheet adds assumed scramble yards, assumed designed-run yards, and a small chosen sack-rate adjustment. It then applies a normal distribution with a fixed spread. The outputs illustrate that model; no dataset establishes them as a real Bo Nix forecast.

Undated fixture with unverified analyst, partnership, historical efficiency, opponent rate, market line, and event. No training sample, held-out calibration, current odds, or verified settlement rules are supplied.

Try a worked example

Default inputs produce 4 × 5.2 + 1 × 3.7 + (7.4 − 7.4) × 0.15 = 24.5 example yards. Against threshold 18.0, the gap is 6.5. With assumed standard deviation 11.5, the normal upper-tail calculation is about 71.4%. That percentage follows the chosen spread; it is not an observed hit rate.

Read the curve as a mathematical illustration

NIST defines the normal model by its mean and standard deviation and notes that its assumptions should be tested. Here the spread 11.5 is fixed, not fitted to a supplied sample. The continuous curve extends beyond the displayed 0–50-yard chart, which is a crop rather than a complete outcome distribution. NIST: Normal distribution

A density height is not the chance of an exact yardage. Tail probability corresponds to area beyond a threshold. The code approximates the normal cumulative function, subtracts from one, clamps the result to 1–99%, and rounds to one decimal. The clamp can conceal extreme model outputs. NIST: Normal distribution

The model computes continuous Y greater than the selected threshold. Integer rushing results and differently worded “18+” or “over 18” markets can have different qualifying sets; this worksheet does not implement a verified settlement rule. Its bands at 60%, 70%, and 40% are chosen display thresholds, not betting recommendations.

Check football meaning and missing inputs

The NFL statisticians’ guide distinguishes rushing plays from sacks and yards lost attempting to pass. A larger defensive sack rate does not automatically become extra rushing yards. This worksheet simply adds 0.15 yards for each percentage point above 7.4, without evidence of a causal conversion. NFL: 2025 Guide for Statisticians

Designed runs always use the stored 3.7-yard assumption. The sliders contain no verified event date, play-by-play classifications, attempts forecast, injury/availability inputs, or opponent-specific calibration. Those are missing evidence, not facts supplied by the curve.

Change the threshold to see the tail change without changing the modeled mean. Reset restores all five defaults. The cached Chart.js view samples density every two yards; the native fallback draws a continuous-looking curve and a threshold marker. These views illustrate the same assumed mean and spread.

Download JSON or text to retain the assumptions and source links. A useful real forecast would require dated observations, a justified distribution, and testing predictions against outcomes; the supplied numbers alone cannot establish that validation.

Sources and further reading
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