Since my journey to Norwegian football took the Deila highway through Scotland, my interest in analytics in football saw me develop a projection model for the SPFL. I named my model
Marbz RonnyB.U.R.L.E.Y., very much spitefully named after former Scotland midfielder and current football caveman turned television pundit Craig Burley. B.U.R.L.E.Y. stands for “footBall Using Reliable anaLytics, Even You.” I found it fitting to name the model after Burley since he seems to foam at the mouth at the mere mention of expected goals, and seeing that expected goals is very much behind my model, it only seemed appropriate.

Since I brought B.U.R.L.E.Y. to the SPFL, I thought I might as well try it out for the upcoming Eliteserien season. Since we have the expected goals numbers for the clubs from last season, we can apply the same principals that I used in B.U.R.L.E.Y. to both try and predict the likely point totals for each club and the win probabilities for each individual match. But since I am new to football in the Land of the Midnight Sun, I cannot just bring B.U.R.L.E.Y. to Norway. No, it needs a Norwegian acronym.

I came into Norwegian football with two hard truths. First, Rosenborg is seemingly miles above the rest of the league. Second, Ronny Deila is a shag of a man. Since “trollkids” would be too long of an acronym, we have to go with R.O.N.N.Y.  for our model’s name. R.O.N.N.Y. stands for Relying On Norwegian Numbers Yearly! With the most important aspect of our model set (the name of course), we can get down to the business of predicting this year’s Eliteserien.

I will quickly explain how I developed R.O.N.N.Y. in the next few paragraphs. A lot of people are just here for the predictions and to tell me how correct I am, so if you do not care about the math behind R.O.N.N.Y. skip ahead a bit.

To come up with R.O.N.N.Y., I borrowed Mark Taylor, from the Power of Goals Blog, simulations method with a few tweaks. Using the expected goal data from last season, I took the xG averageSarpsborg.jpg for the league, the average xG both home and away for every club, and the xG for and against for every club to come up with a calculation for xG for every match up between every team in the Eliteserien.

With these expected goal figures, I use a Poisson distribution to come up with the probability of every scoreline for every match up in the league. If we sum the winning score lines for each team in each of these match-ups, we can determine the probabilities of who B.U.R.L.E.Y. thinks will win each match.

Once we have the win probabilities for every match up, we can then run simulations for every game each club has remaining in Excel, as Mark details how to in the link above. We
run each club’s season 1000 times (…and BOY are Frode Kippe’s legs tired). We take the point total R.O.N.N.Y. suggests in each of these 1000 simulations, average those totals, and that is how we have the projected points for each club.

(End of the methodology section, if you were trying to skip the math and were just here for the predictions.)


Are you ready to be absolutely shocked? R.O.N.N.Y. is predicting Roseborg to comfortably win this year’s Eliteserien title. Outrageous, right?! So maybe we do not need fancy math to make Rosenborg Title.jpgthat prediction, however after we go past Rosenborg things start to get a bit more interesting. Last season, Sarpsborg and Stromsgodset finished 6th and 7th respectively. However, R.O.N.N.Y. thinks they under-performed last season and will bounce back and finish 2nd and 3rd respectively. Last year’s 3rd place club Odd will round out the European spots according to R.O.N.N.Y. It looks to be a tight race for those European spots, with R.O.N.N.Y. believing that Haugesund will be the odd (no pun intended) team out of the European places this season.

Dashboard 1.png

There is a bit of a predicted gap between the clubs fighting for Europe and clubs R.O.N.N.Y. is thinking will finish mid-table. Molde, Aalesund, Viking, Lillestrom, Tromso, Valerenga, and Sogndal are all predicted to finish with four points of each other in 6th through 12th. One thing R.O.N.N.Y. does not account for is, well, Ronny. Deila taking over Valerenga is hard to quantify. He has a wealth of experience from a club like Celtic, where winning every week is the expectation and European success is demanded. Will he repeat the same mistakes he committed at Celtic or will he learn from those? This is an area analytics struggles to quantify now, though since we will be updating the expected goals numbers each game and use these to update R.O.N.N.Y., we can get a better idea what the Deila effect will mean to Valerenga.

I previously discussed Brann over-performing their expected goals last season that saw them finish 2nd. R.O.N.N.Y. saw those numbers and thinks they are prime candidates for regression, Brann.jpgprojecting them to finish fourth bottom, just above the relegation playoff spot. Now, since we only had expected goal numbers for the clubs in the top flight last season, I had to do some guessing for the numbers behind Kristiansund and Sandefjord’s R.O.N.N.Y. predictions.

To do this, I took the average expected goal numbers for each factor R.O.N.N.Y. takes into account and use a standard deviation below that average for those clubs. With those numbers, R.O.N.N.Y. thinks that those clubs will struggle. However, despite the promoted clubs predicted struggles, R.O.N.N.Y. still believe that Stabaek will have a worse season than those clubs. Last season, Stabaek had the second worst expected goal difference and R.O.N.N.Y. predicts more pain in Baerum.

Valerenga Viking

We will update R.O.N.N.Y.’s projections throughout the season, as well as publishing win probabilities for individual matches, such as the Valerenga-Viking match in the first week as above. It is looking like R.O.N.N.Y. sees a pretty even match up between the two, with a draw as the most likely result and Viking with a slightly higher chance of winning. With R.O.N.N.Y. being able to improve as the season goes forward, we can both predict results and how the season will go as it progresses.

This article was written with the aid of StrataData, which is property of Stratagem TechnologiesStrataData powers the StrataBet Sports Trading Platform, in addition to StrataBet Premium Recommendations.