football betting data review checklist

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Lazio had won 16 of 18 games in Serie A, with two draws before the break, and were neck and neck with Juventus, which had won the previous eight league titles. They finished the season with just 16 points in the final 12 games and finished fourth, behind Juve, Inter Milan and Atalanta. The Biancocelesti are yet to right the ship this season. They enter this derby eighth in the Serie A table through 17 games, having won just three of their last seven games.

Football betting data review checklist cricket live betting site

Football betting data review checklist

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With sports betting becoming more available, I am intrigued to dive in and learn more about it.

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Claudia wirz bettingen burton Bet type: Barcelona to finish outside the Top 2 Odds: 3. Of course, having better odds for a heavy favorite is a plus, especially if you are a firm believer they will not win by just one. Thanks for the lessons I found them every useful. More grease to your elbow! Wilson, rick. If games are shortened, games posted as final will be accounted for within the betting world, unless stated otherwise. MMA betting - bettors guide.
Wake forest clemson betting While the point spread may include other factors theoretic home field advantage, bettor bias, among other thingsit has long been used in football prediction research as a valid measure for team matched betting tutorialsbya. All studies have limitations. A coach can consider the relative strength of teams and the ability of each team in the red zone to score when contemplating late game actions designed to either force the game into overtime or attempt to win the game outright during regulation time. The research that goes into this can be similar to researching fantasy, and player props are something some players brush over before the start of a slate. We all know this type of punters who place around 20 bets at the bookmaker during the weekend or even a single afternoon.
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A certain player can be badly missed against certain opposition and easily compensated against another. For example, a team missing its best centre back could struggle against an attacking-minded opponent and do well against a side that prefers to sit back. How did their subs perform the last time they played? Are they just bench-warmers or squad players that get enough minutes to be in good shape? Those questions and many more need to be answered.

There is another aspect that is often ignored. The schedule of both teams is essential, as nowadays the number of games played per season demands rotation. This is especially true for the English Premier League due to the lack of a winter break. It should give you a good idea if the manager will start his strongest line-up or try to rotate a bit.

The next step would be to check how both teams performed against each other in the past. Certain fierce rivalries such as the North London Derby between Tottenham and Arsenal, for example, has its own rules and the game often does not depend on form as much.

There is another important thing you might notice — certain teams or even certain players sometimes have a good streak against a specific opposition. Plenty of examples can be seen in the English Premier League where Sunderland is on a great streak against the local rival Newcastle in the past couple of years.

Nowadays, you can find so many opinions on the Internet that could be pretty overwhelming. Still, there are certain experts that could be useful. Whether is successful punters or well-known journalists, there are people who are good at analyzing and predicting football matches. Seeing a different perspective could confirm your own expectations or show you an angle you might have missed. Most of them will provide you the necessary data:. One of the best free odds comparison services in the business.

You will find the best prices for almost any football match out there. It has all the important markets and then some more. On top of that, there are plenty of stats, betting tools, a reliable live score service and even tipsters who provide picks. The OLBG community is probably the biggest when it comes to betting. The platform has many useful features including free tournaments with real prizes, hundreds of successful tipsters and much more.

Consider this article as your starter pack for how to bet on football. We strongly encourage you to build on it by finding your own ways for an even better evaluation of the matches you are willing to bet on. At Football-Bet-Data we provide historical football stats, and upcoming soccer data for matches in over 65 leagues dating back to Our site has been live since and we have built up a good reputation for value for money product and excellent customer service.

All of our data can easily be exported to Microsoft Excel for further analysis or manipulation. Our site is suitable for system creation, back testing data and trend analysis. Existing members have created profitable football systems, and have run blogs using our data archive dashboard to produce the desired criteria.

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A variety of online websites were used to collect relevant game data. The NCAA. Additionally, the data reflecting a team's offensive and defensive red zone performance explained in more detail below was also found at the NCAA website. This data is only available for the past 7 years, thus the 7 years convenience data set. This value indicates which team is favored to win and by how many points and is set by Las Vegas gambling casinos in attempt to level betting amounts on each team.

While the point spread may include other factors theoretic home field advantage, bettor bias, among other things , it has long been used in football prediction research as a valid measure for team strength. One of the seminal past works using point spread data in football game analysis was Stern In essence, he found empirically that the likelihood of a team winning a game is found by the cumulative normal distribution with a value of the point spread, the mean of 0, and a standard deviation of This study chose to transform the point spread data to the likelihood of winning based on this research NORM.

The NCAA keeps track and ranks teams based upon the percentage of times they score in the red zone, including the number of touchdowns six points and field goals three points. Curiously and inaccurately , they rank the teams based on their percentage of scoring instead of the more insightful expected number of points a team scores in the red zone. The defensive red zone performance was similarly calculated for each team, reflecting the expected or average number of points scored when opponents crossed their yard line.

The red zone data was coded from the perspective of the team that chose to go on defense first. As overtime rules gives each team possession of the ball at the 25 yards, just five yards greater than where the hypothetical red zone begins, it is hypothesized that a team's performance measured by RZO may be relevant to the outcome. The implications of investigating this process may be worthy of future research. In a few cases, data was missing regarding team red zone performances normally when non-division I-A teams were involved in an overtime game.

A total of usable data points games were identified. As the goal of the study was to discover actionable rules for the coaches, all games were used in creating the machine learning models i. Given our goal of interpretable, implementable, and understandable results, two machine learning techniques were employed—logistic regression and SAS Enterprise Miner decision tree algorithm. Logistic regression would provide a probability on likelihood for teams to win games in overtime, while a decision tree approach creates a set of rules that could mimic a decision approach to help a coach decide how to tactically alter their end-of-game strategies.

The output of both approaches will be contrasted in the following sections. For the decision tree analysis, all default parameters were used in the Enterprise Miner. This included the use of chi-squared measure for nominal splits and the entropy criteria for ordinal splits. Generic use of the tools would lead to greater ability for replication.

The following tables provide some of the relevant and interesting descriptive statistics of our game database. Table 1 provides basic descriptive information regarding mean, standard deviation, and range. Table 2 shows the win—loss records of the teams that chose to go on defense first, by year. We can see that the data does not support the conventional wisdom held by many that there is a significant advantage to being on defense first. Additionally, Table 2 shows the win—loss records of home teams involved in the overtime games.

As some games were played at neutral sites, the total number of games does not match the numbers in the defense first column. Table 3 shows the distribution of games by the number of overtime periods. The number of games won by the team that went on defense first in the first overtime as a reference point is shown in this table as well. The third period of OT is where a team must go for a two-point conversion after scoring a touchdown.

Next, the two machine learning technique results are presented—logistic regression and decision tree analysis. However, from a predictive standpoint, this makes this a difficult problem. When using logistic regression, the use of an intercept term was considered. When a full model was forced, the intercept value was not significant. When the intercept value was suppressed, and a backward elimination strategy employed, three variables were significant—the normalized point spread NORM , red zone offense RZO , and opponent red zone offense ORZO.

Table 4 shows information about the logistic regression coefficients. Table 5 shows the classification matrix—which classified at a respectable but not outstanding In interpreting the coefficients, it is not surprising that the larger the perceived strength of the team increased normalized point spread and the most effective larger red zone offense expected value, the higher the likelihood of the model predicting a victory for the reference team. Also note the larger negative impact the opponent red zone offense has on the predicted odds.

Game location and any defensive parameters were eliminated as insignificant variables in the stepwise regression. Table 6 assists looking at a few examples to help illustrate additional interpretation of the coefficients. Consider that the average red zone offense expected number of points was almost exactly 5.

So, with both teams performing at that level, if a team was a seven-point favorite, they will have a projected probability of winning the game in overtime of Now consider the situation where the reference team has a poorer red zone offense 4. Using the same 7-, , and point favorite scenarios, the likelihood of the reference team winning decreases to 52, These scenarios are depicted in Table 6.

In practice, a coach would know the historical performance of each team's offense in the red zone and would also be able to assess through point spreads or other means for the relative strength of the teams. Therefore, in real time, they could gain a reasonable estimate on the likelihood of winning a game in overtime based upon this output. The derived rules from the Enterprise Miner used only the normalized point spread and the red zone offense parameters. Using domain knowledge in the spirit of XAI, these rules in general could be further simplified: If a team is favored and their red zone offense is not terrible since 4.

The predictive accuracy is shown in Table 7 While the classification performance was better than the logistic regression approach, the insight provided by the logistic regression seems more value added to the decision-maker. A coach can consider the relative strength of teams and the ability of each team in the red zone to score when contemplating late game actions designed to either force the game into overtime or attempt to win the game outright during regulation time.

Two simple examples illustrate how these results could be used as such in the Discussion section. The two machine learning approaches struggled to predict the outcome of overtime games, though the results were improved over pure chance. Consider though the practical reality of overtime games—after having 70—90 plays from scrimmage during the regulation portion of the game, the outcome of overtime could come down to as few or even less than six plays.

This study once again dispels that being on defense first provides some sort of remarkable advantage to the team that wins the coin toss. The knowledge of this should be comforting to teams that lose the coin flip. Examples might include penalty kicks in soccer, the home team batting last in baseball, and perhaps other scenarios in various sports. Consider a scenario when late in the regulation game, a team scores a touchdown and trails by one point after the touchdown.

Here are two recent examples another convenience sample that illustrate how this approach might be implemented in real time, using the results of this study. Oklahoma State is favored by 10 points. Their red zone offense averages 5. Texas Tech scores late in the game to pull within one point.

What would the results of the study suggest that Texas Tech should do to maximize the likelihood of a victory? Using the logistic regression equation, if they make the extra point and the game goes into overtime, they have a likelihood of between Unfortunately, sometimes making the right decision still leads to an unfavorable outcome.

Oklahoma is favored by 21 points. Oklahoma's red zone offense averages 5. Oklahoma State scores late in the game to pull within one point. They elect to try a two-point conversion to win the game in regulation instead of kicking the one-point extra point to send the game into overtime.

They are unsuccessful and lose. Their coach gets a lot of criticism for going for two points. Using the derived logistic regression equation, Oklahoma State had between a Thus, Oklahoma State appeared to make the right end-of-game decision … even though they too lost! There are likely other similar examples that one can analyze, and perhaps other scenarios where a team may give up the chance for a game-tying field goal toward the end of the game and tries to score a touchdown to prevent overtime.

In summary, the most significant outcome of this study may be the additional insight provided on these end-of-game decisions. All studies have limitations. We have used the past 7 years of data. Seeing that the red zone offense plays a role in predicting outcomes, it will be difficult to go back in the game archives much further without very strenuous play-by-play assessment of hard-to-find game data.

Perhaps there are surrogate measures one can use instead of red zone efficiency that can assist us in gaining further historical insight. Please, make sure that gambling and sports betting is permitted in your country before using the website. Users take full responsibility for gambling on the Internet when using this website.

Using free tips from our website shall be at your own responsibility. Our free tips have only informative character. Learn more about the top online bookmakers in our bookmaker review section here. Odds decimal. Sports Betting Guide How to successfully analyse football matches? Introduction Principles of effective match analysis Comments. Working order. First of all, you have to learn the proper order of analysing the match.

The majority of punters usually select the event first and only after that run or not a quick analysis. We believe this is a huge mistake. Well, simply because you never know in advance that you will find value in the particular match. Therefore, if you would like to become more efficient with your betting, please try to implement our advice into your betting routine.

Number of matches. We all know this type of punters who place around 20 bets at the bookmaker during the weekend or even a single afternoon. The world's best punters also place around the same number of bets , but… per month. Let this serve you as an example. Online sports betting does not like rushing and is nothing like a competition where you have to place more bets than everybody else.

Regularity, efficiency and effectiveness are what matters the most! We are simply not going to be able to go through a thorough analysis of several dozens of matches daily, moreover effectively bet on such a number of events. Therefore, you should rather concentrate on quality, not quantity. You should in no way force betting on anything! There are days or even periods of a year when it is much harder to find value for various reasons that are beyond our control.

When such a situation occurs, you must take a break. You also have to watch out for the weekends, as they bring the most threat to your bankroll. The huge number of weekend matches biases punters to think that there are more opportunities to find value. While it is logical that more matches offer more opportunities, the reality is far from what punters want it to be.

Narrow your focus. If you're still not aware of, football is all about statistics. What is more, this should be your main source of knowledge. Thanks to the nowadays technology, you can find virtually anything you want to know about the particular event on the Internet. Therefore, you should not just stick to checking the scored and conceded goals.

Try expanding your search, start digging deeper, check when teams score goals, when do they concede, how many shots they take, what is their playing style throughout the match, how many fouls do they commit, how many corners to they take, what is their ball possession percentage and so on. The more information you know, the easier it will be for you to place a bet.

The history of matches is a very important element of the pre-match analysis. The matches between the same opponents look rather more similar, especially in the long run. Therefore, sometimes it is enough to catch just one unique feature, which will instantly put you ahead of the bookmaker. Basically, anything that can help you gain that edge over your online bookmaker and place a winning bet.

You must always check what is going on with the playing teams' rosters and their lineups for the particular match. Try to find out whether or not the star players will be featured, who the manager decides to rest and whether or not youngers will start the game instead of more experienced veteran players. Trust us, this information will make a difference for you. Try to pay close attention before important matches. European or national cup runners-ups with great potential are usually resting their key players before the important matchup.

This can highly affect teams' overall performance, therefore, giving you a chance to find some value. The relationship between the coaching staff, management and players highly affects the atmosphere inside the locker room. What is more, it also directly affects positively, as well as negatively the team's chemistry, resulting in a lack of motivation, dedication and competitiveness.

If there is no understanding between the manager and the players, nothing would be able to hold even a billion-dollar roster together. Punters who tightly follow their favourite teams are well aware of such issues. However, if you are not, a good ol' Internet search will come in handy. All you need is a couple of sports websites or forums or even social media platforms like Twitter or Facebook.

Match officials. Unfortunately or not, referees are another quite essential part of the game. Moreover, they quite often steal the spotlight of the match with their officiating. Match importance. Another aspect worth your attention.

It is obvious that the more important the match is, the more prepared both teams are, and the more competitive the game will be. However, there are less important matches, for instance, the end of the season, when teams have already secured their desired position, which was set at the beginning of the campaign.

Such matches rarely provide a competitive environment, however, there are some exceptions. There are also cases, where one team is trying to qualify for the European competition or get out of the relegation zone, while their opponents just want to finish their season in peace.

Such situations usually have a high chance of being corrupted. For a more detailed explanation about the corruption in football, please check out this sports betting guide. Such matches require more cautiousness, therefore, if you do not have any additional information about the match, we suggest rather staying away from it.

Do not look at the odds. Try to analyse a match and all of its possible outcomes without looking at the odds. Punters are often getting biased by the odds offered by the online bookmakers. Always try to assess the likelihood of a particular scenario and find its true value. Find the real odds by yourself and only after that compare them to the ones offered by the bookmaker. This approach will help you easily reveal the bookmaker's mistake and, as a result, provide you with some value.

Social media. Social media platforms are a great source of information that is quite often coming from the first hand, telling us more than any press conferences could ever do. The majority of football players are everyday social media users with highly popular accounts.

They quite often make posts revealing certain inside information that can provide punters with great insight. Another great part of social media networks is that official football clubs' accounts on, for example, Twitter and Facebook usually post official lineups several minutes before the kickoff. This is also very useful if you are looking to place a pre-match wager.

Odds movement. However, in order to become a great punter, you have to try it at least. The best punters are constantly monitoring bookmakers, betting exchanges and their prices. This helps them to be the first to respond to unusual odds fluctuations, which potentially carries lots of value.

While we emphasise the importance of all the above points, the odds movement provides a substantially bigger reward if mastered and utilised properly. We are, in no way, trying to say that giving up on everything else and just sticking to odds monitoring is the best way to become successful in sports betting. Odds monitoring requires a large share of knowledge and analytical skills, the lack of which can severely damage your budget and betting overall.

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