MLS Fantasy Boss A Fantasy MLS Community
By: The Fantasy Physician
Week 11 (KB) and 12 (SS) Big Question – How do the two major Fantasy MLS platforms score top players differently?
In this year of new beginnings, we have focused most here on the Kickbase fantasy MLS game. Like quite a few managers, I have been playing both Kickbase and Sofascore and learning as I go. Every week the same set of MLS players play their real-life matches, and managers on the two platforms will of course notice that performances on the field do not automatically turn into the same relative fantasy point results on the two platforms.
There is much to be said about how strategy in these two games differ (examples: Sofascore uses a bench with manual substitutions and limited weekly player transfers while Kickbase has neither of these features) but this article will focus on a challenge common to managers in both platforms, player selection.
By and large, the best players in real-life are the best best choices in fantasy games, but attentive managers see that some do better on one or the other platform. This week we look at those players that show differences and see that they tell us something about those players but especially that they illuminate what the respective scoring systems emphasize.
Introducing the Kickbase-to-Sofascore ratio (KSR), an analytic of difference in the two platforms
The basic method of this article is to look at the top players – as a whole and position-by-position – and take an extra look at the ones whose results differ most. Both platforms provided limited ranking/statistics information, which are the sources of the data in the tables below. In each case, we construct a composite table of the top 25 players in each platform, sharing their point totals for this season (now entering its adolescence) and a calculated metric, those players’ Kickbase-to-Sofascore ration (KSR). Players are listed in order – highest to lowest – by that metric. The medians for these ratios, among top players, is around 20 ie players earn about 20 Kickbase points for every Sofascore point, but the range is large (12.8 to 30.6) and we will be keenly interested in the players at the edges of that range. Notably, we are focused on totals and not averages; important players who have played less because of injuries may not feature here.
Complicating matters slightly is the fact that some important players are categorized differently in the platforms. In the tables below, these players’ positions in the respective platforms are highlighted in yellow. Highlighted in green are the players with the highest KSR, i.e. ones whose playing style and achievements to date are favored most by Kickbase, and highlighted in blue are their counterparts most favored by Sofascore. A black rectangle at the right identifies the player(s) at the median KSR ie the middle point of the KSR metric. What do we learn?
Top Players – Across All Positions
As you eyeball these tables, be careful to understand that they are ranked by the KSR statistic, not simply who is best in each category for that platform. A good example is Lionel Messi, who has the third highest point total in Sofascore and is the leader in Kickbase. He is a good choice – should probably be in nearly every lineup – in either game and for good reason. But relatively speaking Kickbase rewards him more than Sofascore, just as Kickbase rewarded him more than the legacy FMLS game. Presumably, this is because in addition to goal contributions, Messi (likewise Bouanga) shoots a lot and creates a lot of chances for teammates that generates points for near misses not rewarded as much in Sofascore. The players with low KSR – favored by Sofascore – include a mix of characteristics: defenders with a lot of block and clearance actions and efficient attackers like Guilherme and Nico Fernandez. Quite a number of these players play on teams with worse overall records, reflecting Kickbase’s penalties for losses or Sofascore’s lack thereof. Also, the enrichment of defenders in the Sofascore zone reflects the relative strength of defenders in that platform when compared with Kickbase.
Top Forwards
Looking just at forwards (or at least players designated as such in one or the other platform) we see high KSR for players involved in heavily in chance creation and shot-taking at high-rates, generally on better teams. Players like Prince Owusu, Dejan Joveljic, Kevin Denkey – top scorers on teams that overall do not create a lot of chances and lose a lot of games – are again relatively favored by Sofascore.
Top Midfielders
Midfielders appear to follow some patterns like forwards but also include some players eg Mark Delgado and Sebastian Berhalter who generate points from defensive actions box-to-box play, eg forward zone passes, on good teams. The currently injured Jeppe Tverskov stood out for the same type of success in 2025, but misses the most-favored-by-Kickbase high KSR group this year, perhaps dragged down by overall team performance.
Top Defenders
A first observation from the top defenders’ table is that the median KSR is lower (16.7), a reflection of the fact that Kickbase on the whole rewards defenders slightly less and underscores my ongoing advice to choose Kickbase formations that minimize the number of defenders. Matias Laborda holds the rank of top defender in both platforms with a close-to-median KSR, edged slightly towards Kickbase. He benefits from clean sheets, goal contributions, involvement in attack, defensive actions, and playing on a successful team.
The defensive table will probably need more study to unpack which bonuses and other factors put players in the extremes, and I will welcome managers’ thoughts. Matti Peltola – the extreme of the Sofascore-favored (ie low KSR) players – is categorized as a defender in Sofascore and a midfielder in Kickbase. He benefits less from clean sheets for that reason in Kickbase among other factors. When teams in Kickbase win with shutouts, players get clean sheat bonuses, victory bonuses, and team goals scored bonuses. Zero-zero matches – DC United has two of those – favor Sofascore’s scoring methods.
Top Goalkeepers
If some of the issues with defenders seem opaque, the determinants with goalkeepers are much more clear. Sofascore – as indicated by the blue highlighted low KSR goalkeepers – favors the goalkeepers on the elite teams which have recorded large numbers of shutouts. High KSR keepers – favored in Kickbase and highlighted in green – include keepers from teams with leaky defenses who are tested a lot and accumulate a lot of points from saves, like James Pantemis of Portland.
A broad generalization across all positions: players that make their living from major-category-success – clean sheets and goal contributions – and not too much else are rewarded more in Sofascore. Those achievements matter plenty in Kickbase, but the top players there also accumulate a lot of bonuses from lesser actions and often play on strong teams.
Sizing up Week 11 (Kickbase)/Week 12 (Sofascore)
In the last several weeks I have provided managers with tiered matchup assessments of attacking and defending potential based on sums of expected goals (xG) and expected goals allowed (xGA). I do so again this week. I suggest that these tools are useful for managers in both platforms for different reasons. Because Kickbase rewards inspired near misses on the attacking side, xG reflects the potential for a lot of that and may even predict a good floor for many players. Sofascore managers need “hits” on goal contribution and clean sheets, but those from a probabilistic standpoint are definitionally reflected in xG. Defending tiers may be even more predictive in Sofascore because clean sheets matter more there. All managers will be looking for high average players in good matchups, but a “good” matchup in Kickbase for a defender may represent more of a hybrid of good attacking and defending matchups. Look for defenders in green on both charts or at least two of the following three characteristics: high average and green zone in one of the matchup tiers (and hopefully no red zone).
Attacking Tiers (input data from both tables from Footy Stats)
Defending Tiers (input data from both tables from Footy Stats)
This week Fantasy Strategy Clinic give tools and hopefully food for thought, especially for managers playing both platforms. Fishing tackle but no fish. Challenge yourself to ask “why is this a good matchup?” and not just “which player has a good average in this favorable matchup?” but also “why?”
Godspeed and happy picking!
The “Fantasy Physician” is Ron Birnbaum, @Half Century City on Discord
The “Fantasy Therapist” is Mike Leister, @Kenobi on Discord
The “Fantasy Meteorologist” is Asher Malaschak, @Storminator on Discord
The “Fantasy Gastroenterologist” is Christian Ward, @xward on Discord
