Jēkabpils vs Preiļu BJSS: prediction and stats
1. LigaRegular Season - 19Kick-off on September 9, 2017 at 12:00 AM (UTC)Jēkabpils SC stadions, Jēkabpils
Match cancelled
What is the recent form of both teams?
Over their last 15 matches in all competitions, Jēkabpils have 4 wins, 1 draws and 10 defeats; Preiļu BJSS have 6, 1 and 8.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jul 12, 2017 | RTU(home) | 3-3 | 1. Liga |
| Jun 17, 2017 | Tukums(home) | 5-1 | 1. Liga |
| Jun 10, 2017 | JDFS Alberts(home) | 3-2 | 1. Liga |
| Jun 4, 2017 | Auda(away) | 2-1 | 1. Liga |
| May 27, 2017 | Grobiņa(home) | 1-3 | 1. Liga |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 2, 2017 | Staiceles Bebri(home) | 5-0 | 1. Liga |
| Aug 26, 2017 | RTU(home) | 2-1 | 1. Liga |
| Aug 19, 2017 | Tukums(home) | 2-0 | 1. Liga |
| Aug 12, 2017 | JDFS Alberts(home) | 0-1 | 1. Liga |
| Aug 5, 2017 | Auda(home) | 1-1 | 1. Liga |
What does the head-to-head say?
Across the last 6 meetings: 4 Jēkabpils win(s), 0 draw(s), 2 Preiļu BJSS win(s).
| Date | Score | Competition |
|---|---|---|
| May 21, 2017 | Preiļu BJSS 6-2 Jēkabpils | 1. Liga |
| Oct 2, 2016 | Preiļu BJSS 0-2 Jēkabpils | 1. Liga |
| May 29, 2016 | Jēkabpils 0-2 Preiļu BJSS | 1. Liga |
| Aug 29, 2015 | Preiļu BJSS 0-2 Jēkabpils | 1. Liga |
| May 1, 2015 | Jēkabpils 5-0 Preiļu BJSS | 1. Liga |
| Oct 11, 2014 | Jēkabpils 3-2 Preiļu BJSS | 1. Liga |
Last archived meeting: Preiļu BJSS vs Jēkabpils on May 21, 2017
How many goals should we expect?
Jēkabpils score 1.9 and concede 4.5 goals per match over their last 15; Preiļu BJSS score 2.0 and concede 2.7.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Jēkabpils | 1.9 | 4.5 | 0 | 13/15 | 15/15 |
| Preiļu BJSS | 2.0 | 2.7 | 2 | 9/15 | 11/15 |
Over the last 15 matches in all competitions.
How does Footlab 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. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy