UFC Fight Night: Burns vs. Malott: Predictions & Analysis

Saturday, April 18, 2026·Winnipeg, Manitoba, Canada
Published February 28, 2026
Predictions are for entertainment purposes only and do not constitute financial advice. Please gamble responsibly.

UFC Fight Night: Burns vs. Malott lands on Saturday, April 18, 2026 in Winnipeg, Manitoba, Canada with 3 bouts on the card. Below is our fight-by-fight breakdown, combining Elo ratings, rolling statistical trends, style matchup data, and betting market context into a pick for every bout.

Quick Picks

MatchupPickConfidenceProb
Gilbert Burns vs Mike MalottWelterweightMike MalottLean63%
Jasmine Jasudavicius vs Karine SilvaWomen's FlyweightJasmine JasudaviciusLean63%
Melissa Croden vs Daria ZhelezniakovaWomen's BantamweightMelissa CrodenToss-up55%

Fight-by-Fight Breakdown

Gilbert Burns vs Mike Malott

Welterweight
63%
Mike Malott
Burns
15-8
CO-II1379
All-Rounder
VS
Malott
6-1
CO-II1410
All-Rounder
Method Prediction
KO 32%Sub 36%Dec 32%

The Welterweight matchup features Gilbert Burns (15-8) taking on Mike Malott (6-1). Malott is the bigger frame at 6'1" with a 2-inch reach advantage.

Malott carries a modest Elo edge (1410 to 1379), the kind of gap that reflects a slightly better run of form rather than a talent chasm. Malott has won 3 straight.

Both fighters land in our "All-Rounder" archetype — fighters comfortable everywhere, able to strike or grapple depending on what the opponent gives them. When mirror matchups like this happen, the edge usually goes to whoever can impose their preferred pace and range.

The submission threat is elevated here at 36%, with the model also seeing 32% KO/TKO and 31% decision probability.

The Pick: Mike Malott over Gilbert Burns. The model gives Malott a slight nod at 63% — this could easily go either way.

63%
Jasmine Jasudavicius
Jasudavicius
8-3
CO-II1358
Wrestler
VS
Silva
5-2
CO-III1237
Wrestler
Method Prediction
KO 19%Sub 24%Dec 57%

The Women's Flyweight matchup features Jasmine Jasudavicius (8-3) taking on Karine Silva (5-2).

There's a real Elo separation here: Jasudavicius at 1358 versus Silva at 1237. That 122-point gap typically reflects a meaningful difference in recent quality of competition and results.

Both fighters land in our "Wrestler" archetype — fighters who win by dictating where the fight takes place, grinding out control time and wearing opponents down. When mirror matchups like this happen, the edge usually goes to whoever can impose their preferred pace and range.

A few statistical edges stand out. Jasudavicius throws significantly more leather — a 0.6 sig. strike per minute gap. Jasudavicius is far more active with takedowns, averaging 1.3 more per 15 minutes. Silva has tighter striking defense, making opponents miss more often.

Our method model projects 19% KO/TKO, 24% submission, and 57% decision for this bout.

The Pick: Jasmine Jasudavicius over Karine Silva. The model gives Jasudavicius a slight nod at 63% — this could easily go either way.

Melissa Croden vs Daria Zhelezniakova

Women's Bantamweight
55%
Melissa Croden
Croden
1-1
RK-III1064
VS
Zhelezniakova
1-1
RK-II1111
Method Prediction
KO 25%Sub 38%Dec 37%

The Women's Bantamweight matchup features Melissa Croden (1-1) taking on Daria Zhelezniakova (1-1).

Zhelezniakova carries a modest Elo edge (1111 to 1064), the kind of gap that reflects a slightly better run of form rather than a talent chasm.

The submission threat is elevated here at 38%, with the model also seeing 25% KO/TKO and 37% decision probability.

The Pick: Melissa Croden over Daria Zhelezniakova. This is essentially a pick'em. The model nudges toward Croden at 55%, but there's almost nothing separating these two.

Methodology

Predictions are generated by our ensemble model combining LightGBM (65%) and CatBoost (35%), trained on every UFC fight since 1994. The model uses 23 features including Elo ratings, rolling 5-fight statistical averages, style matchup history, physical attributes, and market odds when available.

On our held-out test set (402 fights from January-September 2023), the model achieves 63.4% accuracy with a log-loss of 0.626. High-confidence picks (>75% probability) hit at 82.7%. For full model transparency, visit our Model page.