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Tu 9Feb aet. Man Utd. West Ham. Weekend matches Fri 5 Feb - Mon 8 Feb. Premier League. Mo 8Feb ft. Crystal Palace. Su 7Feb ft. West Brom. Man City. Sheff Utd. Sa 6Feb ft. Aston Villa. Bristol C. Nottm Forest. Fr 5Feb ft. AFC W'bledon.
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In doing so Football-Data takes the time out of recompiling pages and pages of results data and past betting odds found on a number of football results and odds comparison websites. Download Football-Data's FREE PDF guide to Rating Systems for Match Prediction , and discover how ratings analysis using computer-ready results and betting oddds data can help one to establish a betting edge, as in the chart above right.
Follow when new data are added. Fixtures and betting odds for upcoming games are also are made available, collected Friday afternoons for weekend fixtures, and on Tuesday afternoons for midweeek games. Check out an example datasheet and a set of Notes that describe the available data. The table below provides quick links to all the data files, with descriptions of exactly what data can be found in each data file.
Data files can also be accessed via the country links in the right hand menu. Why not also visit the network partner Tennis-Data for tennis results and betting odds data. CSV format is the industry standard comma delimited file format allowing compatibility with many computer spreadsheet applications. Commas are used to separate columns of data. They will only be seen if a CSV file is opened in a text editor. Football-Data's preferred spreadsheet application is Microsoft Excel, offering a full range of analytical functions to test betting systems developed from match and odds data.
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You might also consider Time Series Lab's Sports Statistics Package for analysing, modelling, and forecasting of time series focusing on sports results. The software allows you to choose from several probability distributions and model specifications to extract sports team strengths from your data.
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International Tournament Cyprus Women. Liga Classic Play Offs. Oberliga Bayern - Relegation. The model aims at accessing more information than results or goals by indirectly deriving it from the betting odds. At the same time, it is a drastic restriction as throughout the calculation of the ELO-Odds ratings no match result is ever directly used.
Moreover, the model uses the betting odds prior to the match as a measure for the actual result, thus only using information that was known prior to the start of the match and fully ignoring the result that is observable after the match.
To make sure this study is based on a solid framework, we make use of previous research and proven statistical methods, that are largely adopted from Hvattum and Arntzen [ 16 ]. As a start value each team is given a rating of 1, points prior to the first match of the first season. To have a useful start value for promoted teams in later seasons, these teams carry on the ratings of the relegated teams. This procedure has two positive effects: First, it can be assumed that promoted teams are in general weaker than the average team in the league.
Thus the ratings of the relegated teams are a more promising estimator of team quality than using an average start value for the promoted teams. Second, it has the nice side-effect that the sum of ratings stays the same over the full period of time, calculated over all teams that are currently participating in one of the four leagues.
These rating differences then are taken as the single covariate of an ordered logit regression model. As a result from the regression model, logistic functions are obtained that transfer a rating difference into probabilities for home win, draw and away win.
Finally, the forecasts are analyzed using the informational loss L i see [ 27 ] for a definition as a measure of predictive quality. Please note that minimizing the informational loss is equivalent to maximizing the likelihood function. To verify whether differences regarding the loss functions of two models are significant, paired t-tests are used.
See Fig 1 for a graphical representation of rating process, forecasting process and testing process. The informational loss for all three models and different parameters is moreover illustrated in Fig 2 , Fig 3 and Fig 4. Second, the actual results in ELO-Result are subject to strong influence of randomness.
A higher adjustment factor does therefore evoke a too strong adaption of the latest results. In general, using the results to choose the parameters i. However, we can see that the results are not highly sensitive to the choice of the parameter s , compared to the sensitivity of the results to the choice of the model see next section.
Table 3 shows the major results of analyzing the predictive quality of the different forecasting methods. Betting odds are shown to have the highest predictive quality, outperforming ELO-Odds on a highly significant level. Therefore, the results of Hvattum and Arntzen [ 16 ] could be reproduced with respect to betting odds, ELO-Result and ELO-Goals, although using a different set of data including four European leagues and two international competitions. The p-value compares each model to the model in the next row.
ELO-Goals being superior to ELO-Result confirms that the goal difference of a match contains more relevant information than its result win, draw, lose. The striking and novel result is the superiority of ELO-Odds to ELO-Goals which confirms that forecasts from previous matches are indeed useful in rating teams and a valuable source of information for forecasting future matches.
The p-value compares each model to ELO-Goals. In fact, this shows that from a predictive perspective the betting odds known prior to a soccer match possess more information than the result known after the match. To put it simple, looking at the betting odds prior to a match gives you more relevant information on team quality and more valuable insights to performance analysis than studying the results afterwards.
This result might partly be driven by the fact that the result of a match is a realization of the underlying probability distribution, while the betting odds represent this probability distribution. Including other match-related quality measures besides results and goals such as expected goals calculated from match statistics after a match could serve as basis for a useful additional ELO rating.
Unfortunately, this would either require a publicly available source of expected goals covering the whole database or a database including comprehensive match statistics in order to calculate own measures of expected goals. By design, we cannot expect the ELO-Odds model to provide better forecasts than the betting odds itself, as these are the only source of information for the model.
Nevertheless, it is worth evaluating why there is such a clear gap in predictive qualities. Note that, although using betting odds as a source of information, the ELO-Odds model by far is exploiting less information than the betting odds. It can only extract team specific information from the betting odds and aggregate them in the ratings. Motivational aspects of a single match or any relevant information like injuries or line-ups that has become available in between two matches will not be reflected in ELO-Odds.
Moreover, the actual result of the preceding match is not reflected in ELO-Odds, while it is surely influencing the betting odds. Finally, the ordered logit regression model using the ELO difference as single covariate might be a limiting factor, thus even an accurate rating does not necessarily lead to an accurate forecast. One important aspect of this study is to shed light on accurate predictive team ratings that are usually used as an intermediate result of forecasting models. Betting odds for a match can be seen as the market judgement for the quality of both teams participating.
However, it is not straight forward to obtain a quantitative rating for each team from the betting odds of various matches. By using the betting odds as an input for the ELO calculation in ELO-Odds, we made the information included in the betting odds visible in terms of a team rating. The results of the previous section have already shown that ELO-Odds in general provides a superior estimation of team quality.
We would like to illustrate this with reference to two remarkable examples. Certainly these examples cannot be seen as a proof for the superiority of ELO-Odds, but they can be useful to illustrate differences in quality estimation and how these can be used to understand the quality development of teams.
Before comparing ELO-Odds to ratings based on results or goals, we need to verify that the different ELO measures are comparable at all. Please note that due to the construction of the ELO calculation, points gained by one team are equally lost by another team. Therefore the sum of points for all teams in our database stays constant over the whole period of investigation. As a result, the ratings are comparable in terms of size and it is possible to compare the quality estimation of teams in ELO points between different models.
In particular it becomes possible to analyze differences between ELO-Odds and ELO-Result on a team level and consequently to gain more detailed insights on the quality and performance development of each soccer team. Despite small deviations especially at the beginning of the season , the ratings for ELO-Result and ELO-Odds are mainly in line and virtually no difference in ratings exists at the end of the season.
In February —after having massively unsuccessful results for half a year—Dortmund was in last position of the league table. Consequently ELO-Result shows a drastic decrease of almost rating points. Surprisingly ELO-Odds for a long time hardly shows any reaction to the unsuccessful period, proving that the market judgement of the team quality was only weakly modified. The subsequent development might be interpreted as a confirmation of this judgement as Dortmund was playing a successful rest of the season and finished 2 nd and 3 rd in the two following seasons.
Leicester finished 12 th in the following season, which again fits closer to the cautious market judgement than to the rating based on results. In light of the results of this study, these examples show the effective use of a betting odds based rating in order to gain practical insights into the quality of soccer teams. Moreover, they are impressively showing that soccer results seem to be a very one-dimensional and thus an insufficient reflection of team quality.
This result is in line with Heuer et al. This is the major reason for using hardly definable, but valuable criteria like chances for goals to estimate team quality [ 30 ]. Moreover, it gives rise to the idea of calculating advanced key performance indicators using position data from soccer matches [ 31 , 32 ].
Admittedly, the two examples refer to very special situations and were explicitly chosen in order to illustrate differences in ratings. Moreover, both situations were only discussed very briefly not considering events like the coach of Dortmund announcing to leave the club during the season or possible psychological and motivational effects hampering the performance of Leicester after the surprising championship.
The usual perception would be that after 38 matches the teams are fairly well ordered related to their underlying quality throughout the whole season. As a comparison the teams were ordered following the average ELO-Odds rating during the season and presented at the right side of the table. There is a strong similarity between both rankings, but likewise there are a few notable discrepancies.
Atletico Madrid won the title although clearly being ranked in third position by the betting market behind FC Barcelona and Real Madrid. Given the outstanding role of FC Barcelona and Real Madrid, this result might not be surprising and will be in line with the perception of many soccer experts, coaches and officials at that time.
Differences concerning less successful teams are more interesting. According to the market valuation Levante UD was the worst team in the league during this season although finishing 10 th in the league table. In contrast to that, Betis Sevilla was ranked 11 th by the market, but in fact was relegated at the end of the season.
This comparison gives valuable insights to the difference between results and market valuation of teams. Certainly, we do not have full knowledge about the exact mechanisms of performance analysis in professional soccer clubs. From an outside position and following the detailed media coverage, however, it seems that results are by far the most important basis of decision-making.
Under the background of this study, club officials should pay more attention to careful performance analysis by assessing various sources of information than solely looking at the results when evaluating the work of players and coaches. When investigating a quantitative model for forecasting soccer matches, a common approach is to examine the financial benefit of the model by back-testing various betting strategies and calculating the betting returns.
For reasons of completeness and comparability to other studies, betting returns for different ELO models were calculated and can be found in S1 File. However, we would like to point out that gaining positive betting returns cannot be equated with a superior predictive quality of the underlying model as measured by statistical measures. However, it would certainly not be judged as a valuable probabilistic forecasting model. This example illustrates that finding profitable betting strategies and finding accurate forecasting models are slightly different tasks.
In addition, ELO-Odds is intended to connect the advantages of betting odds and mathematical models by extracting information from betting odds and using them in mathematical models. Consequently it would—by design—be unreasonable to expect systematically positive betting returns from such a model. Based on these reasons, the focus of this study is on evaluating the predictive quality of a forecasting model in terms of statistical measures and its benefit in enabling insights to performance analysis.
Although the predictive power of betting odds is widely accepted [ 23 , 11 ], betting odds have not been used as a basis to create rankings and ratings. Lots of effort has been made in developing mathematical models in order to find profitable betting strategies and thus beat the betting market [ 1 , 20 , 16 ]. In contrast, we pursue the strategy of using betting odds as a source of information instead of trying to outperform them.
As the results show, this is a promising approach in an attempt to extract relevant information that would be hardly exploitable otherwise in mathematical models. We could successfully transfer prior results concerning ELO-ratings in association soccer [ 16 ] to a different set of data including both domestic and international matches.
This transferability of results should not be taken for granted as the structure of the data heavily depends on the choice of teams and competitions. The data set used here is characterized by full sets of matches within the leagues and—in relation to this—only a few cross-references i. See Fig 7 for a simplified illustration of the database as a network of teams nodes and matches edges.
Please note that for purposes of the presentation an explaining example is demonstrated, instead of the full database. The aforementioned study was missing international matches and different countries, but including lower leagues. Yet another situation applies for national teams who are playing relatively rarely. Tournaments as the World Cup take place only every four years and are played in a group stage and knockout matches. Further matches in continental championships or qualifications are lacking matches with opponents from different continents.
In other sports or comparable contexts such as social networks the structure again might be completely different. For data sets like the one used within this study, the ELO rating system might not be the optimal approach as it is not designed for indirect comparison. Each match directly influences the rating of both competitors and thus can indirectly influence the future rating of other teams.
However, a match is never directly influencing the rating of a non-involved team. We would expect a notable benefit in treating teams and matches as a network and taking advantage of this structure for future rating approaches.
It can be supposed that this will lead to a shortened time period to derive useful initial ratings and more accurate quality estimations, especially for teams not being part of cross-references i. So far, only few attempts to make use of the network structure [ 33 ] or explicitly including indirect comparison [ 34 ] have been made in US College Football. Other methods like the Massey rating see [ 35 ] for an introduction can be argued to implicitly take advantage of the network structure.
However, there is a lack of general theory and a theoretical framework that investigates the best rating methods for different types of network structures. Another aspect contributes to the complexity of evaluating rating and forecasting methods.
The quality of a rating and forecasting model such as ELO-Odds depends both on its ability in estimating team ratings and its ability to forecast the outcomes, given accurate ratings. As match results are affected by random factors, the true quality of a team is never known or directly observable and thus the quality of the rating can only be tested indirectly.
Moreover, it can be assumed that the true quality of a team will be subject to changes over time. In view of this, it is difficult to prove which aspect of the model carries responsibility for achieving or not achieving a certain predictive quality. To gain better insights into the quality of rating models, it will be useful to conduct further studies using a more theoretical framework.
This could be achieved by constructing theoretical data sets including known team qualities true ratings and simulated data for the observable results, applying the rating models to this data set and then comparing the calculated ratings with the true ratings. ELO-Odds provides clear evidence for the usefulness of incorporating expert judgement into quantitative sports forecasting models in order to profit from crowd wisdom. Further evidence for the power of expert judgement can be found in Peeters [ 20 ] where collective judgements on the market value of soccer players from a website are successfully used in forecasting tasks.
Moreover, researchers recently have started attempts to extract crowd wisdom from social media data. An example aiming at soccer forecasting can be found in Brown et al. Within this study we made use of betting odds as a highly valuable tool in processing available information and forecasting sports events. The betting odds themselves are a measure for the expected success in the following match. Using our approach, we can directly map these expectations of the market to a quantitative rating of each team, i.
This measure proves to be superior to results or goals when used within a framework of an ELO forecasting model. We did not evaluate the differences between ELO-Odds and the betting odds themselves in detail. Future studies investigating match related aspects such as motivational aspects, line-up, etc.
In contrast to prior research, we emphasized that rating methods and forecasting models can help to gain insights to the underlying processes in sports and that there is a strong link between forecasts and performance analysis.
The present study is further evidence that results and goals are not a sufficient information basis for rating soccer teams and forecasting the outcomes of soccer matches. Expert opinion can possess highly valuable information in forecasting, future rating and forecasting models should become more open to include sources of crowd wisdom into mathematical approaches. In times of social networks and online communication new possibilities have emerged and will keep emerging.
Huge data sets from social media e. Twitter data or search engines e. Google search queries have just been started to be explored in the scientific community and are a challenging, but highly promising approach to be used in rating and forecasting. With respect to the methods and results shown within this study, a measure based on betting odds would be more suitable than the aforementioned measures based on results, goals or league tables.
This could be adapted in future research by taking advantage of the ELO-Odds rating as an improved method to assess team qualities. Appendix including details on calculating probabilities from betting odds Appendix A and the investigation of betting strategies Appendix B. Data set including the minimal data needed to replicate the study as well as main results ratings intended to be usable by other researchers in future research.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds.
Funding: The author s received no specific funding for this work. Introduction Forecasting sports events like matches or tournaments has attracted the interest of the scientific community for quite a long time. The sources can be broadly classified in four categories: Human judgement, i. Mathematical models, i.
Betting odds, i. Human judgement Numerous works have investigated the predictive quality of human forecasts in soccer. Rankings The predictive character of rankings is questionable for several reasons. Mathematical models A frequently investigated and widely accepted mathematical approach in sports forecasting is the ELO rating system, which is a well-known method for ranking and rating sports teams or players.
Betting odds Betting odds can be seen as an aggregated expert opinion reflecting both the judgement of bookmakers and the betting behavior of bettors. Download: PPT. Transferring betting odds to probabilities Betting odds are widely used to derive forecasts as they are simply transferrable to probabilities and have proven their quality in a large number of different studies. Rating systems The ELO rating system is a well-known and widely used rating system that was originally invented to be used in chess, but has successfully been transferred to rate soccer teams cf.
Then the parameter k is modified to be Therefore, the model is able to use more information than the pure result of a match. ELO-Odds Although betting odds have proven to possess excellent predictive qualities, they have not been used as a basis to create rankings and ratings. Then the actual result as used in ELO-Result is replaced by: The model aims at accessing more information than results or goals by indirectly deriving it from the betting odds.
Statistical framework To make sure this study is based on a solid framework, we make use of previous research and proven statistical methods, that are largely adopted from Hvattum and Arntzen [ 16 ]. Fig 1. The forecasting methods and statistical framework as used within this study and largely obtained from Hvattum and Arntzen.
Fig 2. Average informational loss for various choices of the parameter k in model ELO-Result. Fig 3. Average informational loss for various choices of the parameters k and lambda in model ELO-Goals. Fig 4. Average informational loss for various choices of the parameter k in model ELO-Odds. Table 2. Comparison of informational loss for different models and various parameters. Predictive quality Table 3 shows the major results of analyzing the predictive quality of the different forecasting methods.
Table 3. Statistical tests comparing the predictive qualities of different forecasting methods. Table 4.