Marseille, Lyon, and a dive into Ligue 1 pseudo-xPG data
January 12th, 2025by Mohamed
In comparison to last season, Ligue 1 has had a banner campaign in terms of parity and at this time last year, Paris Saint-Germain had a five point lead over second place Monaco and both Olympique Lyonnais and Olympique de Marseille were at least 15 points behind PSG in the title race.
Marseille and Lyon were no fewer than at least eight points back of third place LOSC Lille for the final Champions League qualification spot.
Fast forward to this season and the entire complexion of the league table is flipped on its head:
Olympique Lyon has had a 14 point difference relative to last season and have had their goal difference rocketed to +26. Marseille were the leaders for the majority of the season and have had a +10 difference in goals scored relative to last season. Meanwhile PSG are nine points behind the pace they set last season.
There's been substantial change in Ligue 1 in terms of how the standings look though goal scoring on the whole hasn't moved as much as the amount of craziness would otherwise tell.
Despite the offensive explosions of both Olympique Marseille and Olympique Lyon, Ligue 1 is averaging 2.43 goals per game which is a slight upgrade over the 2.395 mark last season, though it's still the lowest among the top five European leagues.
Explaining the difference in team performance from one year to the next is something that football is striving for, a never ending journey filled with narratives and deviation. As football has evolved over the last couple of seasons, so has the data for it, which has helped in going past cliches put forth by the punditry in media.
We've gone from the use of the use of metrics like TSR (total shots for/ total shots for+ total shots against) and PDO (Conversion percentage + save percentage) pioneered by people like James Grayson to much more intricate data uses like Ben Pugsley's measures of football teams in different game states, which helps in going past what Grayson help built and allows us a deeper look at how teams behave when down two or up two.
Also another type of metric that's come up has been perhaps the most fanciest of new data trends in football: expected goal data, which takes into account shot location and the type of pass that's preceded the shot.
Expected Goal Ratio compared to Total Shot Ratio did a better job in its repeatability according to 11tegen11. From Michael Caley to Colin Trainor to 11tegen11 to Paul Riley, ExPG has taken the baton from TSR and helped build upon it. But why listen to me blabber about it when you can just watch this articulate it so much better?
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