AVAILABLE PLAYER DATA
(UP TO SEASON-END 2025)
| PLAYER | SEASON 3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 | S12 | S13 | S14 | S15 | S16 | S17 | S18 | S19 | S20 | S21 | S22 | S23 | S24 | Cumulative Impact Score | Avg Year-End Impact Score | All-Time Peak Impact Score | Award Score | Total Seasons Played | Greatness Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lionel Messi | 8216 | 12336 | 15540 | 15568 | 15978 | 11143 | 11275 | 8597 | 3837 | 1599 | 830 | 2642 | 5096 | 5173 | 4600 | 3446 | 3812 | 4730 | 4338 | 4338 | 7303 | 15978 | 35 | 21 | 106281 | |||
| Cristiano Ronaldo | -1231 | -5568 | -2746 | -1216 | -2772 | -388 | 2677 | 224 | 1103 | -935 | 874 | 4482 | 2955 | 3039 | 3053 | 2314 | 2015 | 2885 | 3172 | 2714 | 2978 | 2978 | 935 | 4482 | 23 | 23 | 52512 | |
| Ronaldo Nazario | 16644 | 29910 | 27875 | 25931 | 24940 | 22436 | 20485 | 19046 | 18843 | 17963 | 16982 | 16807 | 16827 | 16952 | 14855 | 14855 | 20433 | 29910 | 11 | 17 | 92596 | |||||||
| Diego Maradona | 14193 | 13200 | 12631 | 12814 | 13728 | 13212 | 14599 | 14673 | 12592 | 11230 | 12563 | 15249 | 15080 | 9674 | 9807 | 10226 | 11697 | 10424 | 10424 | 12644 | 15249 | 7 | 20 | 48645 | ||||
| Neymar | 7926 | 16502 | 15970 | 7997 | 10201 | 6543 | 10055 | 10444 | 9014 | 7249 | 4375 | 6259 | 4922 | 4922 | 9035 | 16502 | 16 | 15 | 39376 | |||||||||
| Ronaldinho | 15674 | 12434 | 10984 | 12869 | 10391 | 10378 | 9328 | 8059 | 4482 | 4336 | 5936 | 8087 | 8189 | 6915 | 7693 | 7693 | 9050 | 15674 | 13 | 17 | 56672 | |||||||
| Zinedine Zidane | 4678 | 10079 | 29861 | 29721 | 29144 | 24391 | 20100 | 18136 | 20013 | 16320 | 14997 | 13773 | 13510 | 12922 | 12922 | 18403 | 29861 | 13 | 16 | 89593 | ||||||||
| Johan Cruyff | 17364 | 17559 | 20906 | 22959 | 24031 | 19252 | 19019 | 21334 | 19914 | 17864 | 17974 | 18512 | 17727 | 16542 | 16373 | 16373 | 19155 | 24031 | 17 | 17 | 157727 | |||||||
| Kylian Mbappé | 4029 | 10103 | 10229 | 10662 | 19237 | 17848 | 19042 | 15347 | 15347 | 13312 | 19237 | 11 | 10 | 56272 | ||||||||||||||
| Erling Haaland | 6066 | 17066 | 16064 | 16385 | 11788 | 8765 | 8765 | 12689 | 17066 | 6 | 8 | 14024 | ||||||||||||||||
In soccer, comparing players across different eras is very difficult, because of the great scarcity of stats in earlier decades. You cannot make a direct comparison with advanced stats between players of today and those of decades ago. However, soccer happens to be a very dynamic game and no particular stats can determine if one player is better than another anyway. At times a player can be essential to a team despite not having the most goals or assists per game. Conversely, players with many goals or assists do not consistently lead to better outcomes for their teams. It is the overall arsenal of skills of each player then which determines who is best or most impactful to his team. If we add to this how many titles a player has won, we can get a very good picture of the greatest players: those with the highest team impact who also won the most titles.
How do we measure a player’s impact then? Well, if we consider goal differential (goals scored minus goals conceded) per season during the moments the player is on the field for his team per game, we have a very good idea of how instrumental this player is, especially if we consider every season for an entire player’s career, for club and country. That is a lot of goal differential data, which is significant. If we then compare a player’s goal differential to that of his team when without the player (club and country) across his entire career, we can really tell how much better the team performed with the player as opposed to without. It is then possible to generate numbers from this data per season, which are the ones posted in the above table. Positive numbers mean the player had a positive net impact on his teams that season and negative numbers mean the opposite: they were a detriment to their teams that season for whatever reason. Obviously, the players with the highest positive impact across their entire career are the best in this category. It is worth noting here that for the purposes of measuring a player’s maximum effort in the games played, only the official games were used to calculate goal differential. All friendly and exhibition games are excluded from this assessment.
Now we move on to measuring titles won. How do we measure this category? Well, if we assign number values to different titles depending on their relative importance, we can also standardize these titles to any player even across generations. All players throughout history have played similar competitions: a domestic league regular season, a continental club/national team competition, the Olympics, the FIFA World Cup, etc. These values can of course vary potentially depending on the relative perceived worth of one competition compared to another. I decided to assign the value of 1 to winning a player’s domestic league each season. This is the most basic competition to win for a player each year and is reproducible throughout the world. Then comes a continental competition, which again is reproducible around the world, and generally holds more worth than winning a domestic league. I gave it a value of 2 as if it were equivalent to winning 2 domestic leagues. Then, there are the club world cups, which generally involve playing just one or a few games to determine the best club in the world each year. Given that the opposition are the strongest teams in the world but also weighing how brief it is, I also gave it a value of 1 (equivalent to winning a domestic league). The Holy Grail for a soccer player though is winning the FIFA World Cup with their country. Given that this competition occurs every 4 years and how notoriously difficult it is to win, I gave it a value of 4 (equivalent to winning 4 domestic leagues!). The continental national team competitions occur every 2 years and received a value of 2. Lastly, the Olympics also occur every 4 years but not nearly as important to win as the World Cup; hence I assigned to it a value of 3. The total value of a player’s titles won across his entire career are posted under the column “Award Score” in the table above.
The main detractors of the goal differential method to evaluate a player’s impact usually contend that because the player’s goal differential is affected by the type of teammates he has while playing, it is therefore useless to measure an individual player’s ability accurately. This is partially correct. However, because we are extrapolating across all the games in a season, across all the seasons in a player’s career, club and country, the effect that a player’s teammates can have on his overall goal differential is quite minimal. Teammates change across seasons multiple times and even within the same game. What in a few games would cause potential dilution of a player’s performance, would get significantly mitigated over a large amount of games and across time. In accordance with this, a minimum of 90 minutes of official games played in a season by a player and by his team without him were necessary for me to compare their goal differentials that season. Anything less than 90 minutes for an entire year can create excessively positive or negative goal differentials that would not be realistic in a complete 90-minute game, and hence not a realistic measure of the player’s impact.
Having said that, there is a potential serious issue that I became aware of while gathering the data. While a large amount of data can reveal a player’s true performance more accurately, it is still impacted heavily by the quality of his teammates, which makes comparisons with other players problematic. I realized that players with a low quality of teammates (lower goal differential when without the player) tended to have an overinflated impact score (and vice versa: a player with high quality teammates could have a diminished impact score). The overall quality of teammates can remain very similar for an individual player across one or more seasons. If a player has low quality teammates, they will enhance his impact, because they will consistently perform poorly when without the player. Conversely, when a player is surrounded by world-class talent, the teammates will dilute his impact by performing very well most of the season. This can make it seem falsely that the player with great teammates is not as impactful as other players in weaker teams. I therefore made sure to factor the teammates’ overall goal differential per season when without the player into the player’s final numbers, enhancing his impact if the goal differential is high and diminishing it if the goal differential is low.
There is also the matter of adjusting for a player’s quality of opponents. Different leagues or competitions provide different levels of opposition, which can impact the goal differential in the player’s games. However, because we are comparing a player’s goal differential with that of his teammates without him across games from the same league or competitions, once several games and seasons are taken into account, the level of opposition is so similar for both parties that the player’s goal differential becomes a more realistic measure of his true performance no matter what league or competition he plays in.
As we can see then, although the goal differential method is not perfect, it can produce useable data that, when applied equally to all players across history, can effectively be used to compare their performances to a large extent. Any issues with differences in quality of teammates and opponents can be mitigated by the vast amount of games in which the player’s individual performance is compared to that of his teammates without him.
In the above table, I post the cumulative impact score for each player after each season, starting with the 3rd season since enough cumulative data has been gathered by then to be truly significant for comparisons. This is the net positive or negative impact the player was having on his teams (club and country) up to that season’s completion. Obviously, after all the seasons are taken into account, this final score is their final cumulative impact score for their entire career (the Cumulative Impact Score column). The players with the highest cumulative impact scores across an entire career played the best overall soccer as evidenced by the significant positive impact they had on their teams during all that time. The All-Time Peak Impact Score column is self-explanatory and essentially lists the highest impact score the player had at any time in his career. This is technically a measure of the best that the player was able to play soccer at any time relative to another player. The Avg Year-End Impact Score is the average of the posted cumulative impact scores each season. The difference between this and the cumulative impact score is as follows. A player who had a steady mediocre but positive impact from youth to old age will have a similar cumulative impact score at the end of his career as another player who had a significantly negative impact for the first few years of his career and then improved markedly at some point to begin generating a much better impact later on (or vice versa). However, the Avg Year-End Impact Score may reward the player with the posted steadier positive year-end impact throughout his entire career as opposed to the one with posted negative impact, which would drag down the average from the years with positive impact. This score is meant to show the players that were more skilled from the beginning of their careers (more talented) compared to those that improved over time (but not as naturally gifted).
The Greatness Score column is then the final value that determines which players are overall better than the others, and the one that crowns the greatest player of all time if they have the highest score. It is a combination of the cumulative impact score after an entire career adjusting for the amount of seasons played and the Award Score earned during that time. I believe this is the best method for determining the best soccer players in history. Nevertheless, I am interested in ways that you believe the methodology can be adjusted further to better reflect a player’s impact score. Please let me know your thoughts and suggestions below.