Baltic Power Rankings after GW8: Flora return to the 1000+ club while Kalev and Grobiņa gain places despite losing

Baltic Power Rankings after Game week 8 (GW8) saw some interesting movements as clubs such as Estonian Kalev and Latvian Grobiņa, who lost 9-0 and 3-0 respectively, managed to gain places… At the same time, FC Flora returned to the 1000+ coefficient club and Auda finally overtook Paide to claim 6th place in the Baltics. Harju and Kuressaare fell to the bottom, sharing the last place in the rankings while both Tammeka, Dainava, Super Nova and Narva Trans lost a place each and Kalju rose by one position.

Perhaps the most surprising outcome of GW8 was Tallinna Kalev who, despite losing 9-0 to the defending Champions Levadia gained 2 places in the rankings, rising up to 26th for the first time since the start of season. How come? Well, this has to do with the way coefficients are calculated, so it might be worth delving a little bit deeper into the mechanics of it.

Firstly, the Baltic Power Rankings shares just under 21000 coefficient points between the 30 teams. These points were derived from assigning each club a base rating and then adjusting it based on a range of multipliers that reflected relative strength of the three leagues, individual club performances over the past 5 years, UEFA coefficients and some other metrics. Once the season started and teams began to play one another, their individual coefficients began to change based on relative performance. So, if a team with a coefficient of, for example, 500 would play a draw against another team with the same or sufficiently close coefficient, their individual ratings would not change because the mathematical model predicts that a game of football between similar strength teams should end in a deadlock. Conversely, if a team with a coefficient of 1000 faces off a team with a coefficient of 500, they are predicted to win easily and, as a result, will only ‘win’ 3 coefficient points while the weaker team will ‘lose’ the same amount. This is because the model expects much stronger teams to win over much weaker teams. However, if a 500 coefficient team pulls of an upset over a 1000 coefficient team, the mathematical model will adjust heavily and subtract 22 points from the stronger team while giving the same amount to the weaker team. And so on and so forth.

Because Tallinna Kalev are a much lower ranked club than Levadia, the model expected them to lose, which they did. As a result, the impact on their coefficient was minimal (they only lost 5 points). Tartu Tammeka, who were in 26th after GW7, on the other hand, were ranked higher than Harju but lost to them, thus also losing 13 coefficient points. Similarly, Dainava lost to Banga. While Banga are a higher ranked side, they are significantly higher ranked, so, having defeated a team of broadly similar strength, the mathematical model awarded them with 11 of Dainava’s coefficient points. This is why Kalev, despite suffering a huge loss, managed to jump two places in the rankings!