Sporting Kansas City and Austin FC carry enough attacking threat to stretch this contest, while fragile backlines invite mistakes. Their combined chance creation points toward an open MLS battle, with defensive lapses making goals beyond the initial exchange the most likely outcome.
Match GoalsOver 2.5
Best Pick
BTTS
60%
Correct Score
Top
1-1
10.8%
8.00
Top
2-1
9.4%
9.50
1-0
8.2%
13.00
1-2
7.1%
11.00
2-0
7.1%
15.00
0-1
6.3%
15.00
2-2
6.2%
11.00
3-1
5.4%
15.00
147%
2.20TOP
X24%
3.90
229%
2.90
1X71%
X253%
1276%
Combined Bet Builder
4 legs
Sporting Kansas City or draw looks the logical side: home xG is 1.71 against Austin’s 1.63, while Austin’s defense is 26% weaker than average. Their combined 3.34 xG supports Over 2.5; 10.10 expected corners and 3.91 expected cards also point toward Over 8.5 corners and Over 3.5 cards.
1Double ChanceSporting Kansas City or Draw
2Match GoalsOver 2.5
3Corners Over/UnderOver 8.5
4Total CardsOver 3.5
Probable XI3-5-2
Kortkamp
Borges
Karic
James
Bartlett
Agyabeng
Johnsen
Ramos
Bassong
Luiz
Harris
Impact Player Hot Head Shooter Bad Boy
André Luiz — the go-to finisher in this side, and he loves these nights
Stephen Afrifa — always sniffing around the box — the kind of player who just ends up on the scoresheet
Taylor Calheira — always sniffing around the box — the kind of player who just ends up on the scoresheet
Jake Davis — plays on the edge, and the referee usually notices sooner or later
Moisés Mosquera — lets emotions run high in big moments — one late tackle away from a booking
Zorhan Bassong — known for a few words too many with the officials
Lineups not yet announced — full squad analysis below
Sporting Kansas City29 players
52.5/ 99Average
Top Rated
1Jansen MillerDEF80
2Moisés MosqueraDEF76
3Zorhan BassongMID67
Austin FC26 players
48.0/ 99Average
Top Rated
1Brandon Vazquez FWD72
2Daniel PereiraMID65
3Owen WolffMID65
Sporting Kansas City35
PlayerOverall RatingImpactAggressionDiscipline
Jansen Miller80767564
Moisés Mosquera76877049
Zorhan Bassong67658252
Kwaku Agyabeng65516870
Lasse Berg Johnsen62744449
Jayden Reid61327590
Ethan Bartlow60712866
Ian James60414391
Or Blorian60554078
Dejan Joveljić60523690
Jake Davis57346770
Macielo Tschantret54475261
Stephen Afrifa53517647
Austin FC31
PlayerOverall RatingImpactAggressionDiscipline
Brandon Vazquez 72626491
Daniel Pereira65654767
Owen Wolff65407191
Jorge Alastuey62564772
Jon Gallagher57335482
Mateja Djordjevic55466555
CJ Fodrey55455772
Jayden Nelson54518047
Brad Stuver5253950
J. Rosales52414967
Oleksandr Svatok52693043
Nicolás Dubersarsky52486747
Damian Las51523950
Pre-Match Predictions· Lineups not yet announced
Top Goalscorers
Attackers with highest impact
1
Brandon Vazquez
Austin FC
Impact
62
2.75
2
Stephen Afrifa
Sporting Kansas City
Impact
51
2.89
3
Taylor Calheira
Sporting Kansas City
Impact
49
2.75
4
André Luiz
Sporting Kansas City
Impact
45
2.21
5
Myrto Uzuni
Austin FC
Impact
42
2.89
6
Shapi Suleymanov
Sporting Kansas City
Impact
41
3.42
Card Candidates
Lowest discipline + high aggression
1
Mikkel Desler
Austin FC
Disc
18
Aggr
58
3.96
2
Myrto Uzuni
Austin FC
Disc
1
Aggr
36
8.20
3
Zorhan Bassong
Sporting Kansas City
Disc
52
Aggr
82
3.58
4
Stephen Afrifa
Sporting Kansas City
Disc
47
Aggr
76
7.70
5
Taylor Calheira
Sporting Kansas City
Disc
1
Aggr
29
7.70
6
Ilie Sánchez
Austin FC
Disc
1
Aggr
28
3.96
Player Rankings
Snipers
Top goal threats
1
Brandon Vazquez Austin FC
62
2
Dejan JoveljićSporting Kansas City
52
3
Stephen AfrifaSporting Kansas City
51
4
Jayden NelsonAustin FC
51
5
Taylor CalheiraSporting Kansas City
49
Wall
Top defenders
1
Moisés MosqueraSporting Kansas City
87
2
Jansen MillerSporting Kansas City
76
3
Ethan BartlowSporting Kansas City
71
4
Oleksandr SvatokAustin FC
69
5
Jon Bell Austin FC
68
Control
Top playmakers
1
Lasse Berg JohnsenSporting Kansas City
74
2
Manu GarcíaSporting Kansas City
70
3
Zorhan BassongSporting Kansas City
65
4
Daniel PereiraAustin FC
65
5
Ervin TorresAustin FC
56
Bad Boys
Most likely to get booked
1
Taylor CalheiraSporting Kansas City
1
Aggression 29
2
Ilie Sánchez Austin FC
1
Aggression 28
3
Myrto UzuniAustin FC
1
Aggression 36
4
Mikkel DeslerAustin FC
18
Aggression 58
5
Besard SabovicAustin FC
23
Aggression 46
Power Rankings
Strongest overall
1
Jansen MillerSporting Kansas City
80
2
Moisés MosqueraSporting Kansas City
76
3
Brandon Vazquez Austin FC
72
4
Zorhan BassongSporting Kansas City
67
5
Kwaku AgyabengSporting Kansas City
65
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Sporting Kansas City vs Austin FC based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the Sporting Kansas City vs Austin FC match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If Sporting Kansas City has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Sporting Kansas City vs Austin FC without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Sporting Kansas City a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.