Sagamihara vs Kitakyushu: result 1-0 and analysis
J3 LeagueRegular Season - 4Kick-off on April 1, 2017 at 04:00 AM (UTC)Sagamihara Gion Stadium, Sagamihara
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What was the result of Sagamihara vs Kitakyushu?
Sagamihara won 1-0 on April 1, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Sagamihara have 2 wins, 4 draws and 9 defeats; Kitakyushu have 3, 6 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Mar 25, 2017 | Tokyo U23(away) | 0-1 | J3 League |
| Mar 20, 2017 | Gamba Osaka U23(away) | 1-0 | J3 League |
| Mar 12, 2017 | Parceiro Nagano(home) | 0-1 | J3 League |
| Nov 20, 2016 | Kataller Toyama(away) | 1-1 | J3 League |
| Nov 13, 2016 | Fujieda MYFC(home) | 3-1 | J3 League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Mar 26, 2017 | Cerezo Osaka U23(home) | 1-0 | J3 League |
| Mar 12, 2017 | Blaublitz Akita(home) | 1-1 | J3 League |
| Nov 20, 2016 | Montedio Yamagata(away) | 0-3 | J2 League |
| Nov 12, 2016 | Mito Hollyhock(home) | 2-2 | J2 League |
| Nov 6, 2016 | Machida Zelvia(home) | 1-3 | J2 League |
How many goals should we expect?
Sagamihara score 0.8 and concede 2.0 goals per match over their last 15; Kitakyushu score 0.7 and concede 1.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Sagamihara | 0.8 | 2.0 | 1 | 9/15 | 8/15 |
| Kitakyushu | 0.7 | 1.1 | 5 | 5/15 | 5/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
Sagamihara sit 5th in J3 League (18 points from 10 matches) and Kitakyushu sit 16th (10 points from 10 matches).
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 5 | Sagamihara | 10 | 5 | 3 | 2 | 18 | 12 | 18 |
| 16 | Kitakyushu | 10 | 2 | 4 | 4 | 12 | 18 | 10 |
A question about Sagamihara vs Kitakyushu?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
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How does Footlab AI compute this prediction?
The model combines both teams' Elo ratings (anchored by competition pool), their recent form weighted by opponent strength, home advantage and a Poisson matrix for scorelines. When bookmakers publish odds for the match, their consensus is built into the calculation: that is what proved most accurate on past matches. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy