The FIFA World Cup is not only the biggest sporting event in the world – it‘s also one of the most popular events to bet on. During the month-long tournament, billions of dollars are wagered on everything from individual match results to the outright winner of the World Cup trophy.
For those interested in sports analytics and data science, this betting activity generates a treasure trove of valuable data in the form of betting odds. By web scraping and analyzing historical World Cup betting odds, we can gain fascinating insights into the tournament, uncover betting market inefficiencies, and even build predictive models to forecast match outcomes.
In this guide, we‘ll walk through the process of using web scraping to collect FIFA World Cup betting odds data, with a particular focus on the 2014 World Cup. We‘ll then explore some of the insights and applications of this scraped data.
Understanding Betting Odds and Their Insights
Before we dive into web scraping, let‘s review how betting odds work and what they represent. Odds are a numerical expression of the likelihood of an outcome occurring, as assessed by a bookmaker or betting market. The higher the odds for a given outcome, the less likely it is considered to occur.
For example, a footballer scoring odds of 1.25 have a much higher chance of occurring than an outcome with odds of 5.0. Odds can be expressed in different formats like decimal, fractional, or American style, but they are all just different ways of representing probability.
The key insight from betting odds is that they provide a data-driven, quantified view of the likelihood of different outcomes, as determined by a market of participants risking real money. The betting market is essentially a prediction market, aggregating the assessments of a large population of people putting their money where their mouth is.
By analyzing betting odds, we can see:
- Which teams are favored or expected to win
- How the market‘s assessment of outcome probabilities evolve over time as new information emerges
- Which results would be considered an upset or surprise relative to market expectations
- Potential inefficiencies in the market, where the odds may not accurately reflect the true probability
So in short, betting odds provide a fascinating real-time window into collective assessment of World Cup match probabilities through the lens of a financial market. Now let‘s look at how we can collect this valuable data at scale.
Web Scraping World Cup Betting Odds
While you could manually check odds on individual bookmaker sites, this would be extremely tedious to do for hundreds of World Cup matches across many bookmakers. The solution is web scraping – programmatically extracting the odds data from betting sites for collection into a structured database.
There are many different web scraping tools and methods, but a common no-code approach is to use a visual web scraping tool like Octoparse, ParseHub, or OutWit Hub. These tools make it easy to visually select the data you want to extract and set up automated scraping without needing to write code.
For our example, we‘ll walk through using Octoparse to scrape FIFA World Cup 2014 betting odds from the odds aggregator OddsPortal. OddsPortal collects and displays odds from dozens of bookmakers, making it an ideal one-stop source to scrape odds from instead of individual bookmaker sites.
Here are the steps:
- Install Octoparse and start a new scraping task
- Enter the OddsPortal World Cup 2014 results URL: https://www.oddsportal.com/soccer/world/world-cup-2014/results/
- As the page loads, Octoparse will detect and highlight the main data elements, like the date, teams, score, and 1X2 (three-way) average odds.
- Select the data you want to extract and click "Create Workflow"
- Octoparse will generate a workflow of the steps to scrape the selected data from the page. You can customize this workflow if needed.
- Run the scraping task, and Octoparse will walk through the pagination and extract all the historical odds into a CSV or Excel file
And that‘s it – you now have a structured dataset of FIFA World Cup 2014 betting odds to analyze. With a bit more work, you can set up ongoing scraping tasks to collect live odds for upcoming matches as well.
Analyzing the 2014 World Cup Odds
Now the fun begins – digging into our scraped World Cup betting odds data to extract insights. Let‘s look at a few interesting analyses of the 2014 tournament.
First off, we can see which matches had the most lopsided odds – in other words, the biggest favorites. The odds are telling us Germany was a huge favorite against Brazil in their infamous 7-1 semifinal blowout. Other big favorites who advanced were Netherlands vs Mexico and Colombia vs Uruguay in the Round of 16.
We can also find the biggest upsets of the tournament by looking for match results that defied the odds. By this measure, the biggest shocks were Spain losing to Netherlands 5-1 in the group stage at odds of 19.0, and defending champs Spain then losing to Chile at 16.0 odds to get eliminated. Other major upsets included Germany‘s 4-0 trouncing of Cristiano Ronaldo‘s Portugal in their opening match.
It‘s also interesting to see how the outright winner odds shifted throughout the tournament:
- Before the tournament, Brazil were heavy 3.5 favorites, followed by Argentina 4.5, Germany 6.0, and Spain 7.0
- After the group stage, Brazil were 3.25 favorites, followed by Argentina and Germany both at 4.0
- Before the semifinals, Germany passed Brazil as the 2.5 favorites after their dominant performances
- Going into the final, Germany were 1.85 favorites over Argentina‘s 2.05 to lift the trophy
In the end, the betting market favorite from the semifinals onward won the tournament in Germany. But there were plenty of upsets and surprises along the way creating profitable betting opportunities for those on the right side of the results.
Additional World Cup Betting Markets
While much of our analysis focused on the odds to win individual matches (known as 1X2 or three-way odds), there are many more World Cup betting markets available to analyze. Other popular markets include:
- Handicap/spread: Odds on a team winning by a certain margin, similar to point spreads in US sports betting
- Over/under goal totals: Odds on the total number of goals scored in a match going over or under a set line
- Half-time/full-time: Odds on the leader at halftime and the match winner, e.g. Brazil/Draw
- Correct score: Odds on the exact final score of the match
- Goalscorer: Odds on players to score first, last, or anytime during the match
Looking into odds for these markets can provide an even richer, more nuanced view of the betting market‘s expectations and biases. For instance, handicap odds can indicate how dominant a favorite is expected to be, while goalscorer odds tell us which players are considered most likely to find the back of the net.
Advanced bettors and analysts even use odds from multiple markets to reconstruct exact probability estimates of various score lines. These market-implied probability distributions can then be used in modeling and simulation analysis.
Predictive Modeling with Betting Odds
Perhaps the most interesting application of scraped betting odds data is using it to build predictive models to forecast match outcomes. While the odds themselves are already predictive in a sense, we can combine them with other data sources like team ratings and past performance in a machine learning model to create an even more accurate prediction.
There are many different approaches to modeling soccer match outcomes, but a common one is to use a Poisson regression model to estimate the expected number of goals scored by each team. Poisson regression is well-suited for modeling goal-scoring since goals are discrete events that occur at a relatively low rate.
To build such a model, we would combine our scraped betting odds data with other features like the teams‘ Elo or SPI ratings, recent results, head-to-head performance, and so on. The betting odds can be used as an input feature directly or calibrated into an implied win probability first. We would then train the model on past match data to learn the patterns and relationships between these features and goals scored.
Once trained, this model can then be used to predict the number of goals each team will score in a future match and calculate a forecasted win-draw-loss probability. By comparing our model‘s predicted odds to the market odds, we can find value bets where the model disagrees with the market‘s assessment.
Of course, building an accurate and profitable soccer prediction model is easier said than done, as the betting market is very efficient. But using advanced data like our scraped odds and sophisticated techniques like machine learning, it‘s possible to gain a statistical edge.
Conclusion
In this post, we‘ve seen how web scraping FIFA World Cup betting odds can provide a valuable source of data for analysis and modeling. By programmatically extracting and storing historical odds data from sources like OddsPortal, we can uncover insights into market expectations, biases, and inefficiencies surrounding the tournament.
Looking back at the 2014 World Cup odds, we identified some of the biggest upsets that defied the market‘s expectations, like Spain‘s shocking group stage exit and Germany‘s 7-1 demolition of Brazil. We also saw how the outright winner odds evolved throughout the tournament in reaction to results.
Beyond just analyzing the odds data in isolation, we looked at how it can be used as an input into predictive models to forecast match outcomes. By combining betting odds with other data like team ratings in a machine learning model, we can build powerful predictive models to identify betting value.
Of course, the World Cup is just one of many sporting events with vibrant betting markets to scrape and analyze. You can apply this same approach of web scraping, analysis, and modeling to any sport and league with available online betting odds. With the right data and techniques, you can gain a true edge in sports betting through the power of web scraping and data science.