Imagine playing Monopoly. You land on Boardwalk ten times, but every time a lucky dice roll saves you from paying rent. At the end of the evening, you're declared the winner -- but were you really the best player? That's exactly what happens every week in the Bundesliga, the Premier League, and every other league in the world.
The official table only counts points. Three for a win, one for a draw, zero for a loss. Simple, brutal, final. But it conceals something crucial: How well did a team actually play?
A team can create 20 chances, hit the post three times, the opposing goalkeeper plays the game of his life -- and in the end they lose 0-1 from a deflected shot. Three points gone, despite an outstanding performance. The table says: Loser. The statistics say: Actually the better team.
That's exactly where ehrlichetabelle comes in. We take the Expected Goals (xG) and Expected Points (xP) of every match and calculate where each team would stand if luck and misfortune didn't distort the results. The result is a table closer to the truth than what you read in the newspaper every Monday morning.
On average, tables deviate by 3 to 8 positions. That means at least a third of all teams sit at a position they don't deserve based on performance -- either too high or too low. Some teams have up to 15 points more or fewer than their xP in a season. That's the difference between Champions League qualification and a relegation battle.
ehrlichetabelle currently tracks five leagues: Bundesliga, Premier League, Serie A, La Liga, and the Swiss Super League. For each league, we show data from the last five seasons. So you can see not only where your team stands today, but also whether it has consistently over- or underperformed over the years.
Picture a penalty kick. The taker stands alone, eleven meters from goal, only the goalkeeper in the way. Historically, about 76 out of 100 penalties go in. So a penalty has an xG value of 0.76. The chance is good, but not certain.
Now imagine the opposite extreme: a player shoots from 35 meters out, at full sprint, with their weak foot, two defenders blocking the path. Historically, a shot like that goes in maybe 2 out of 100 times. xG: 0.02. A chance? Yes. A good chance? The statistics clearly say no.
Expected Goals measure exactly that: the quality of every single scoring chance. Each shot is analyzed -- distance to goal, angle, game situation, whether it was a header, whether the player was running. This produces a number between 0 and 1 that says: How likely would a goal be from this exact situation?
The sum of all xG values in a match gives a team's Expected Goals. If Bayern Munich has chances worth 2.8 xG in a game, then an average team would have scored roughly three goals from those chances. Whether Bayern actually scored 0, 2, or 5 goals is another story entirely -- and that's what makes it fascinating.
A concrete example: On February 22, 2026, AC Milan lost at home to Parma 0-1. Sounds like a poor showing from Milan, right? A look at the xG tells a different story: Milan racked up 2.72 xG, Parma only 0.95. Milan should have won the match comfortably based on chances created. Instead: zero goals from top chances. Football is sometimes brutally unfair -- and xG makes that unfairness visible.
The xG model was originally popularized by analysts in 2012 and has since revolutionized how professional clubs, scouts, and media think about football. Today, virtually all top clubs in the world use xG data for tactical analysis, transfer decisions, and match evaluations.
Once we know how many goals a team should have scored from their chances (xG), we can take the next step: How many points should the team have earned from the match?
The calculation sounds more complicated than it is. Take a game where Team A has 2.1 xG and Team B has 0.8 xG. Team A created significantly more and better chances. But how likely is a win? A draw? A defeat?
This is where probability comes in. Based on both teams' xG values, we simulate how this match would play out across thousands of parallel universes. In our example, Team A wins perhaps 65% of the time, there's a 20% chance of a draw, and Team B wins in 15% of cases.
Expected Points are then calculated simply: win probability times 3 points plus draw probability times 1 point. For Team A: 0.65 x 3 + 0.20 x 1 = 2.15 xP. For Team B: 0.15 x 3 + 0.20 x 1 = 0.65 xP. Team A "deserved" about 2 points, Team B less than one.
Over an entire season, these xP add up. And this is where it gets exciting: the difference between actual points and xP shows how much luck or misfortune has influenced a team's season. A team with 45 actual points but only 38 xP? They've gained 7 points through luck or exceptional efficiency. A team with 30 points but 37 xP? They've lost 7 points through bad luck -- and might be due for a winning streak.
Why? Because luck and misfortune tend to even out over time. Teams that strongly underperform often improve in the second half of the season. Teams riding a lucky streak frequently fall back. The xP table is therefore not just a look at the past, but also a glimpse into the future.
"Statistics lie" -- you hear that often from football romantics. But with xG, the opposite is true. There's hardly a metric in sport that more reliably captures true playing strength than Expected Goals. Here's the evidence.
The most famous football story of the 21st century: Leicester City wins the 2015/16 English title at odds of 5000:1. A miracle, no question. But what do the xG say? Leicester had significantly fewer xG than the top teams that season. Their xP table would have placed them around 6th to 8th. What Leicester had: Jamie Vardy and Riyad Mahrez converting far above average, and Kasper Schmeichel saving shots that statistically should have gone in. The following season? A relegation battle. The xG had predicted it.
Another example: SC Freiburg in the Bundesliga 2023/24. Freiburg sat comfortably in the upper half of the table, but the xP told a different story. Their chance quality wasn't good enough for a top-half position. By season's end, Freiburg actually slipped -- the regression to the mean that xG analysts had predicted.
Research backs this up: A 2019 study analyzed over 15,000 matches across Europe's top 5 leagues and found that xG predicts a team's future performance better than the actual results of the last 10 matches. In other words: if you want to know how a team will perform in the coming weeks, look at their xG, not their points.
The reason is simple: goals are rare. In a typical football match, only 2 to 3 goals are scored. With so few events, randomness plays an enormous role. A ball that flies three centimeters further left hits the post instead of the net. A defender slips, and suddenly it's 1-0. xG filters out this noise and reveals the signal: the actual quality of play.
Of course, there are limits. Teams with exceptional individual players can consistently slightly exceed their xG. Lionel Messi, for example, consistently scored more goals than his xG suggested throughout his career -- he simply wasn't an average player. But even with Messi, actual goals were only about 15-20% above xG. For entire teams over a season, a sustained deviation of more than 10% is extremely rare.
The bottom line: xG isn't perfect, but it's the best tool we have to see through the fog of randomness and recognize a team's true quality. And that's exactly what ehrlichetabelle does -- for five leagues, every matchday, in real time.
Expected Goals (xG) measure the quality of scoring chances. Each shot receives a value between 0 and 1, indicating how likely a goal is from that position. An xG of 0.3 means: from 10 identical chances, you'd statistically expect 3 goals. xG is important because it shows how well a team actually plays -- regardless of whether the ball goes in or not.
Expected Points are calculated from the xG values of both teams in a match. Based on the xG difference, the probability of a win, draw, or loss is simulated. These probabilities are then converted into points: win probability x 3 points + draw probability x 1 point = Expected Points.
That's exactly what the xP ranking on ehrlichetabelle shows. If your team has a better xP rank than their actual rank, it means: based on their chances, your team deserved more points. The difference between actual rank and xP rank shows how much luck or misfortune has influenced the position.
The difference between actual points and Expected Points (xP) measures the luck factor. A team with 30 points but only 25 xP has won 5 points through luck or above-average finishing. Conversely, a deficit shows bad luck or poor finishing. Over an entire season, luck often evens out -- but not always.
xG shows the quality of chances, not the outcome. A striker can miss a 0.9-xG chance (almost a sure goal), or score from a 0.05 chance (very difficult). Goalkeeper performance, composure, and pure luck also play a role. The difference between goals and xG shows a team's efficiency.
Overperformance means a team scores more goals than xG values suggested. This could indicate outstanding strikers, a world-class free-kick taker, or simply luck. Long-term, strong overperformance is hard to maintain -- teams tend to regress toward the xG average.
Underperformance means a team scores fewer goals than expected. The chances were there, but the ball didn't go in. This can be due to poor finishing, bad luck (hitting the post), or strong opposing goalkeepers. Good news: underperforming teams often improve when luck normalizes.
xG is a strong indicator of true playing strength. Teams with high xG values and good xP are more successful long-term. However, individual moments, injuries, and psychology also decide matches in football. xG helps identify favorites but guarantees nothing.
This happens when finishing was poor or the opposing goalkeeper played outstandingly. If your team had high xG but scored few goals, it was bad luck or inefficient finishing. The xG confirms: the performance was good, just not the result. Such games often turn around in the long run.
xG (Expected Goals) measures individual scoring chances -- how many goals a team should have scored. xP (Expected Points) converts xG into match results -- how many points a team should have earned from a game. xG is goal-based, xP is result-based.
Each provider uses their own models and data sources. Some only count shots, others also include attack danger and possession. FootyStats uses an advanced approach that also considers dangerous attacks without shots. Small differences are normal -- but the trend should be similar.
xG shows a team's true playing strength. If the xP table differs significantly from the actual table, betting odds might be misjudged. An underperforming team could be undervalued. Note: xG is only one factor -- injuries, motivation, and form are not included.
The honest table sorts teams by Expected Points (xP) instead of actual points. It shows where teams should stand based on their actual playing performance -- without the influence of luck in chance conversion. A team high in the xP table plays well, even if actual points don't (yet) reflect that.
Statistically, luck and bad luck even out over many games -- but this can take half a season or longer. Individual teams can have bad luck for an entire season. That's why the xP table is so valuable: it shows true quality before luck evens out.
The team with the largest negative difference between actual points and xP has the most bad luck. On ehrlichetabelle, you can see this in the plus-minus column. A value of -5 means the team deserved 5 more points.
The team with the largest positive difference between actual points and xP. A high positive number means the team has more points than expected -- through good finishing or luck. Caution: such teams might slump in the second half when luck normalizes.
xG is most reliable in leagues with good data collection. In top leagues like the Bundesliga or Swiss Super League, data is very accurate. For smaller leagues, data quality can vary. FootyStats covers over 1,000 competitions and provides xG data for smaller leagues too.
The points progression shows how consistently a team plays. Many fluctuations indicate inconsistency. A team with a steadily rising xP curve is improving. The comparison between actual points and xP over time shows whether there were lucky or unlucky phases.
Short-term yes, long-term unlikely. Teams with world-class strikers (like Messi or Ronaldo once were) can slightly overperform. But even they have fluctuations. For most teams: strong over- or underperformance evens out over 1-2 seasons.
Own goals typically don't factor into xG calculations since they don't represent an offensive chance by the attacking team. They count toward actual goals but not xG. This sometimes explains differences between xG and actual results.
Yes, penalties have a fixed xG value of about 0.76 (76% scoring probability based on historical data). A team with many penalties has higher xG. Some analysts also show 'xG without penalties' to evaluate playing performance without set pieces.
This column shows the rank difference between xP rank and actual rank. A positive value (+3) means the team is 3 places better in the honest table. A negative value (-2) means the team is ranked 2 places higher than deserved due to luck.
Look at the xG trends. If xG per game is increasing, chance quality is improving. If xP is also higher than actual points, expect results to improve soon. The points progression on ehrlichetabelle visualizes exactly that.