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Bout 11 of 13

Chad Anheliger vs Steven Koslow

Sat, Oct 17, 5:00 PM ET · Bantamweight · 3 rounds · UFC Fight Night: Buckley vs. Malott · Rogers Place

Started · not saveable

Simulation story

6 chapters · read from the published artifact
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› Loading the matchup model› Reading the outcome distribution› Preparing the score scenariosPress Play to step through what the model expects, chapter by chapter, or read the report below.

Chad Anheliger vs Steven Koslow

Bantamweight, 3 rounds on UFC Fight Night: Buckley vs. Malott. The model read both fighters' histories.

model ufc-fight-model@e657fc40b64dno run count recordedproduced Oct 17, 10:02 AM ET

How much history is behind this. Steven Koslow has only 1 tracked UFC bout in our corpus (fewer than the 2 we count as known), so this read leans on Chad Anheliger's 5 tracked bouts and on league-average priors for Steven Koslow. Treat it as weaker than a bout where both fighters have at least 2 tracked bouts.

Winner

Who the model expects to win

Probabilities over the two fighters, from the head that was evaluated on its own preregistered bar.

Chad AnheligerChad Anheliger13-8-0
vs
Steven KoslowSteven Koslow6-1-0
Model pickSteven Koslow · 64.9%Decision · round 3 or later (54%)
13-8-0Record6-1-0
5Tracked bouts1
35.1%Win chance64.9%

The model reads it 65% — nothing in either tracked record separates them cleanly, so the number rests on the model's wider inputs rather than a headline stat.

Method

How it ends

Read among fights that end with a winner — a draw or no-contest sits outside this sample, so the three add to 100%.

KO / TKO32.7%
Submission19.3%
Decision48.0%
Round

How far it goes

R3+ carries every fight that reaches the judges as well as every round-3-or-later finish.

30
R1
15
R2
54
R3+
Round 1
30.1%
Round 2
15.5%
Round 3 or later
54.5%
Goes the distance
48%

“Goes the distance” is the method head’s decision probability, not a separate model — so it cannot disagree with the bar above it.

The fighters

What the corpus knows about each of them

Tracked bouts only — a fight outside the corpus is not counted, and a thin record says so.

Chad Anheliger
13-8-0
Tracked record

2-3 in the last 5 tracked bouts. 1 of 2 wins came by finish (50%); 3 of 5 tracked fights reached the judges (60%).

Strengths · weaknesses
− 60% of tracked bouts are losses (3 of 5)
Tendencies
· Often goes the distance — 3 of 5 tracked fights reached the judges
Last 5 tracked
LCody GibsonDecision R3
WCharalampos GrigoriouDecision R3
LJose JohnsonSubmission R3
LAlatengheiliDecision R3
WJesse StraderKO / TKO R3
Steven Koslow
6-1-0
Tracked record

0-1 in the last 1 tracked bout. No tracked wins; 0 of 1 tracked fight reached the judges (0%).

Not established
? Only 1 tracked bout — too few to name a strength, weakness or tendency
Last 1 tracked
LCameron SaaimanKO / TKO R3
Method

Where these numbers come from

Three separate heads — winner, method and round — each fitted on a corpus of tracked UFC fights and each tested on past fights it never trained on before it was published. This is an experimental model: winner forecasts are graded on every new card, method and round are not yet graded, and it has not been shown to have an edge over the market.

Winner 60.6% accurate over 3,557 held-out fights · baseline 50% · log loss 0.6615 against 0.6931
Method 52.7% accurate over 3,557 held-out fights · baseline 46.7% · log loss 0.9692 against 1.0255
Round 60.6% accurate over 3,557 held-out fights · baseline 55.3% · log loss 0.9148 against 0.9549
model ufc-fight-model@e657fc40b64d
corpus 8,642 fights · 1994-03-11 to 2026-08-08 · scrape_ufc_stats (GPL-3.0)
generated

Educational and paper-only — not betting advice.

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