Mezőkövesd-Zsóry vs Csákvári TK: match statistics

I back the home win (1) — Mezőkövesd concedes only 0.85 goals per home match on average and records clean sheets in 54% of home games, while Csákvári concede 1.23 on the road and have 23% clean sheets. Visitors, however, show better recent attacking form: Csákvári scored 11 goals in their last 5 matches, averaging 1.31 goals away, whereas Mezőkövesd managed only 5 goals in the same period and average 0.85 at home. Quality of chances is similar — home xG 1.26 vs away xG 1.33 — but Mezőkövesd controls the game more often (55% possession) and produces more on-target shots at home (3.92 vs 3.5), which favors chance creation. Csákvári compensate with higher shot conversion (12% vs 9%), yet a high draw rate away (46%) and fewer clean sheets limit their chances for a full victory, hence the proposed distribution 46%/32%/22%.

I back BTTS - Yes. Both teams show high seasonal BTTS rates: Mezőkövesd 64% and Csákvári 62%, with visitors at 80% BTTS in their last 5 matches versus hosts 40%. Mezőkövesd averages 1.71 GF and concedes 1.43 GA per match; Csákvári averages 1.31 GF and concedes 1.23 GA, indicating both sides create and concede chances. xG and shot metrics confirm this: hosts xG 1.68 vs opponents’ xG conceded 1.73, visitors xG 1.33 vs opponents’ xG conceded 1.48; Mezőkövesd also records 5.29 shots on target vs visitors’ 3.5. Low clean sheet rates (21% hosts, 23% visitors) reduce the likelihood of a No Goal, while the league average of 2.51 goals/match and the teams’ individual profiles (seasonal 64%/62% and visitors’ last-5 80%) increase the probability of both teams scoring. I estimate BTTS at ~72% vs 28% No BTTS, as shots, xG and seasonal BTTS rates give a stronger signal for Goal/Goal.

xG and match statistics indicate a higher-scoring game: home attack xG is 1.68 and away defense xG 1.73, implying about 1.71 expected goals for Mezőkövesd; similarly, away attack xG 1.33 vs home defense xG 1.48 yields about 1.41 for Csákvári, totaling approximately 3.12. A Poisson distribution with λ=3.12 gives probabilities of over 1.5 around 82%, over 2.5 around 60% and over 3.5 around 38%. This is supported by seasonal rates: home teams had over1.5 in 93% of matches, away teams 69%, and over2.5 at 50% and 54% respectively. Additionally, the hosts average 13.71 shots and 5.29 on target versus 10.67 and 3.5 for the visitors, and the both teams to score rates are 64% (home) and 62% (away), which increases the likelihood of goals from both sides. Based on this I recommend Over 1.5 as the strongest signal.

Hosts led at the break in 36% of their home matches, half-time draws occurred in 43%, and visitors led at the break in 21% (36%, 43%, 21%). Over the full match, hosts win at home in 43% of fixtures with xG 1.68 versus the visitors' 1.33 and an advantage in shots on target 5.29 vs 3.5, indicating the hosts’ real attacking strength. By half-time the hosts average 0.79 goals versus the visitors’ 0.69, so when the hosts hold a lead at the break (36% of cases) the model projects a high conversion of that lead into a win (~85%), making the 1/1 scenario the strongest signal (approx. 30%). A half-time draw with a full-time draw (X/X ~19%) and a half-time draw with a home win after the break (X/1 ~11%) are the next most realistic variants given the 43% frequency of half-time draws and the hosts’ 43% home win rate. For these reasons I recommend 1/1 as the best HT/FT selection.