• Bruin Sports Analytics

Which of the top five European leagues is truly a “farmer’s league”?

By: Surya Dham and Nilay Patel

Source: FBref

Debates rage endlessly over Twitter across fans of every European league, proclaiming that their own league is the best. This is usually defined by having teams that tend to do well in Europe while simultaneously having a competitive domestic league that often has down-to-the-write title races and teams jostling near the top. One of the most common insults thrown around is always along the line of “your league is a farmer’s league,” insinuating that the league is dominated by a couple of powerhouses, and that the other clubs employ “farmers” instead of professional athletes, allowing for these clubs to steamroll their way to title after title. With that in mind, we set out to ask: which of the top European leagues are actually farmer’s leagues?

The idea of a farmer’s league is all about competitiveness, and that can be measured in a few ways. Most notably, the domestic success (or dominance) of top clubs within the league is a key metric, a way for fans to see how often other clubs challenge the top teams in their respective leagues to keep the league competitive and unpredictable. If a team usually wins the league by blowout margins, well ahead of the chasing pack, it would be fair to describe the league as a farmer’s league, as no one is able to mount any sort of meaningful resistance to the club’s success. Thus, one important way to characterize the “farmeriness” of a league is by measuring the points gap between the champions and the other top 6 teams (consisting of mainly top-tier clubs vying for qualification to European competitions) over the past 10 seasons to see how competitive the league is historically.

The graph shows a few interesting trends that suggest “farmeriness” is not as easy to characterize. The most notable example is LaLiga: the league has been characterized by the dominance of Real Madrid and Barcelona over the last 10 or so years, especially with star athletes Cristiano Ronaldo and Lionel Messi. The data backs up this narrative: the two-horse title races in LaLiga are, on average, the most competitive among Europe’s top five leagues by some distance, with points gaps usually under 7 points. However, the data paints a different picture overall: as we go further down the table, past the dominance of Barcelona, Atletico Madrid, and Real Madrid, the league goes from being one of the most competitive and tightly contested to one of the least; teams placed 5th, 6th, and 7th are nowhere near the top of the league, and get increasingly further from the top spot, suggesting that the league is dominated by a few big clubs and then smaller ones. By contrast, the Premier League and Serie A, which are fairly similar in this graph, appear to be more competitive in every position, hugging the bottom of the trendlines for nearly every position, starting and ending near the bottom. This suggests that the gap between the top team and the six or so teams below them is much smaller than in other leagues, meaning that these leagues are far more competitive than leagues like La Liga, and can be declared to be the least “farmer-y” leagues among Europe’s top 5. Just above these two is Ligue 1, which is considered a fairly stereotypical farmer’s league; however, the data paints a different picture, denoting that the gap between the top and the six teams below is always less than that in the Bundesliga (but is usually more than that in Serie A and the Premier League), and the quality of the league doesn’t have the drastic drop-off as La Liga when it comes down to teams placing 5th or 6th; this makes Ligue 1 a good candidate for the middle spot, more “farmer-y” than Serie A and the Premier League but less so than the Bundesliga and La Liga. The Bundesliga and La Liga represent two different notions of what it means to be a farmer’s league: La Liga has a few dominant teams but a sudden drop-off in quality, as shown by the initially low level of the curve but the steep slope it takes. On the other hand, the Bundesliga represents a different notion: the gap between the champions and the lower teams is always consistently larger than the other teams (given by the fact that the data points are always higher than every other league, excluding La Liga), but the slope of the graph is relatively flat and starts to level off, which suggests that the competitive nature of the league doesn’t come from its title race but from races further down the table, like the race for 4th place and the last Champions League spot. This means that each of the 5 different leagues offers a different product that makes them each more or less “farmer-y” in their own unique way: competitive races all around the table are the norm in the Premier League and Serie A, Ligue 1 offers a big team to root for that doesn’t always win by a large margin and still leaves room for challengers and more competitive races further down the table, La Liga offers two or three big teams that will have an extremely close race for the title among themselves but offers little chance for the teams below to catch up to the them, and the Bundesliga offers a runaway champion in exchange for far more competitive races for the lower places.

The world of soccer is mired, however, by chance, luck, anomalies, and other abnormalities that mean that things like point gaps and other real variables don’t actually tell the true story of a game. This is where the value of expected goals (xG) comes in. The xG model analyzes the shots taken in a match by each side and takes into account the nature of the shot (the number of defenders, the location of the shot, the positioning of the goalkeeper, and countless other variables. The shot is then assigned a value, which is the xG value for the shot; this value is between 0 and 1, with 0 representing no chance of the shot being a goal and 1 representing a certainty that the shot will be a goal; for example, an xG of 0.5 means that the shot would be expected to be a goal exactly half of the time. Thus, as the xG value increases, so does the likelihood of scoring a goal. To find the xG for a team in a match, the xG values for all of the team’s shots are added up and totaled, yielding their xG; we can also find the expected goals against (xGA) by doing the same for the team’s opponents. Then, we have the expected goal difference (xGD), which is the difference between the expected goals scored and the expected goals against; this number represents the “margin” by which the team should have won or lost. For example, if a game between Manchester United and Watford ends 0-0 but Manchester United have an xG of 2.5 while Watford have an xG of 0.5, Manchester United have an xGD of 2.0, which means that, based on the way they played, Manchester United should have won by 2.0 goals; this indicates that they were dominant and comfortably beating the other team. Thus, a higher xGD correlates to a more dominant performance by a team, and the xGD/90 statistic measures the total expected goal difference over the season (where a team plays against every other team in the league twice) divided by the number of matches the team played, to give an average xGD per game. Of course, better players tend to outperform xG, and it doesn’t always tell the full story; for example, if a bottom-tier team miraculously scores a low xG chance at the beginning of the game and then defends resolutely, allowing the other team to get a lot of low xG chances that don’t go in, the bottom-tier team wins the game despite the xG scoreline, but that is a direct result of the way the team played, and xG doesn’t do a great job of explaining the tactic of defending a lead. Nonetheless, it is still a great indicator of which teams get the ball into useful positions for shots that could lead to a chance on goal, and thus is a good indicator of which teams tend to dominate the matches that they play in.

The following data is taken from the 2017-18 to 2020-21 seasons and includes teams that appear in the top 35 teams across Europe’s top 5 leagues in xGD/90 for each of the seasons; there are 20 such teams across the 5 big leagues.

The graph provides a few remarkable discoveries. Notably, there are 3 teams that appear to dominate their respective leagues: Paris Saint-Germain in Ligue 1, Manchester City in the Premier League, and Bayern Munich in the Bundesliga. This graph paints these three leagues as dominated by these teams over the past 4 seasons, and highlights their dominance of their respective leagues over this time period, leading those leagues to be characterized as the most “farmer-y” for two reasons: the top xGD/90 teams in these leagues are far from any other teams in the same league, and the xGD for these teams is ridiculously high. For these 3 teams, the statistics predict that they will, on average, win every single game in the season by anywhere from 1 to 1.5 goals, which represents a ridiculous level of dominance in the league. In other words, they are a goal better than every other team (the title contenders, the mid-table teams, and the relegation fodder) in the league, on average. By contrast, the graph makes evident that Serie A has 4 teams that are all within touching distance of each other, and three more teams below them that are both close to the top 4 teams and close to each other, yielding an extremely competitive top 7 that provides for an extremely competitive league near the top, and thus yielding a very balanced and “un-farmer-y” league, agreeing with the point gap analysis from earlier. The point gap analysis also supports the position of the 3 LaLiga teams in this graph: there are two teams that are relatively close to each other, followed by a third team that is not too far from them but is clearly an echelon below them, with a marked drop-off in quality. Part of this may be due to Atletico’s defensive style, but both the point gap and the xGD/90 analysis support this interpretation of LaLiga.

Now, we are left with the three leagues dominated by giants; this comes as no surprise to anyone who has followed these leagues over the past four seasons. Ligue 1 is very evidently dominated by Paris Saint-Germain, with only one other team within 0.75 xGD/90 of them; this dominance is drastic and shows a real lack of competitiveness and “farmer-y” nature that’s not even shown by the Premier League or the Bundesliga. Meanwhile, the Premier League has Manchester City at the top, Liverpool around 0.5 xGD/90 behind, and a couple other teams yet further behind as the quality in the league drops off even further; this is indicative of exactly what has been observed over the last few seasons: Manchester City have been utterly dominant, Liverpool have pushed them close with consistently top-tier (but not ridiculous) performances to catch City when they slip up, and the other teams essentially compete for a third place trophy. The Bundesliga also paints a similar picture, where Bayern Munich take the place of City and Dortmund the place of Liverpool; however, the gap between Bayern and Dortmund is even larger and Dortmund are not pushing Bayern the same way that Liverpool push City, instead serving a clear “best of the rest role,” above every other team in the league by a reasonable (but not unfathomable) margin, but well behind Bayern, leading to a dominant league that gives a “farmer-y” quality.

One important place to look, furthermore, to get a glimpse of the competitiveness of the league further down, is how many teams were in the top 35 in one season (say 2020-2021) but not in the 3 other seasons. 5 French teams, 3 English teams, 4 Spanish teams, and 3 German teams were in the top 35 European teams by xGD/90 for the top 5 leagues in 2020-21 but were not present in all of the 3 previous seasons. This suggests that the French league is quite interesting and competitive below the top teams (something also noted in the points gap analysis), the Spanish league a bit less so, and the English and German leagues even less so. The Italian league doesn’t appear, likely due to 7 teams in the league (almost half of the league) being in the chart already, which provides ample competitiveness for the top half of the league; by contrast, only 2 French teams, 4 English teams, 3 German teams, and 3 Spanish teams appeared in the chart, suggesting that the gap between the top and the rest in Italy is not as great as it is in any of the other four countries.

Thus, these two analyses clearly suggest that Italy is the most competitive and clustered league, one that is the least “farmer-y,” with the Bundesliga on the other side of the spectrum being the most “farmer-y,” with Bayern Munich dominating. In between, again, are 3 different types of leagues: the Premier League will offer predictable champions but a bit of chaos just underneath, Ligue 1 will offer pure dominance by one team and another team chasing close in exchange for a more competitive table vying for positions from 2nd to 7th, and LaLiga will offer razor-thin title races between three known teams and a jumble of teams just below. The statistical analyses portray the five leagues in five different styles, and they give credence to fans’ arguments about how other leagues are farmers’ leagues (well, except for Serie A), and they also explain the five different products the individual leagues are selling to millions of fans within Europe and throughout the world.




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