Contact Us

Use the form on the right to contact us.

You can edit the text in this area, and change where the contact form on the right submits to, by entering edit mode using the modes on the bottom right. 


123 Street Avenue, City Town, 99999

(123) 555-6789


You can set your address, phone number, email and site description in the settings tab.
Link to read me page with more information.


Filtering by Tag: betting

Underdogs and Inefficiencies

Ford Bohrmann

Odds makers tend to do a fairly good job in sports-- While they may not be perfect, it tends to be tough to find any consistent exploitable inefficiencies. In other words, it is rare that the odds of "Liverpool winning at home", or some other event like that, are consistently over or underestimated. You may think that the odds in an individual game may be incorrect, but in the long run inefficiencies like that rarely persist. Why? Because bookies would lose money on them. If they realize they are starting to lose money, the odds are going to be adjusted to better reflect the probability of each result occuring.

While I am not really interested in betting on soccer myself, odds do provide an interesting estimate of the probability of an outcome occuring. For example, take Arsenal's home game against Chelsea this past year. Bet365 put the odds of an Arsenal victory at 2.38. These decimal odds imply that they expect the probability of an Arsenal victory to be about 42%. Taking in to account that the odds makers usually lower the payouts so that they make money, the adjusted probability of an Arsenal victory is just over 41.1%.

This is all pretty standard stuff. The odds for relatively evenly matched games like the one above are probably pretty accurate, or at least more accurate than your average person. But what about significant underdogs? What about City against Cardiff? These are a little more difficult to assess. It's clear that Cardiff is an underdog in this game, but how much of an underdog? And do odds makers do a good job of assigning implied probabilities to these lopsided games?

Read More

Goal Time Analysis

Ford Bohrmann

If you had to place a bet, at what minutes do you think the most goals are scored during the course of a soccer game? I was asking myself this exact question, so I decided to try to figure out what the answer was. If scoring is completely random we would expect the distribution of the count of goals scored to be roughly even across every minute of the game. Of course, it is not going to be perfectly distributed because of random errors, but every minute should have roughly the same number of goals, assuming the sample is large enough. I had a hunch that this would not be the case. Specifically, my guess was that there would be more goals scored between the 85th and 90th minutes, whereas there would be fewer in the first 5 minutes of the game. To test this hypothesis, I used data from the Rec.Sport.Soccer Statistics Foundation page from 8 years of the Premiership.
Read More

A Simplified Football Prediction Model

Ford Bohrmann

I recently wrote a blog post for the Betting Expert site about a simple model I created attempting to predict the outcome of football matches using only very simple statistics.

You can read the full blog post here.

I wanted to point out on here something interesting that I found while working on the model; betting odds do a relatively poor job of predicting football match outcomes. In other words, the percentage likelihood of a win, draw and loss for the home team implied from the odds set by bookmakers is surprisingly inaccurate.

My hypothesis for why this happens is that football is very unbalanced, especially in the EPL. It is very hard to predict when an upset is going to happen, mostly because these upsets are (seemingly) random.

Using just 4 factors in my model, including the home team's goal differential for the season up to that game, the away team's goal differential for the season up to that game, the home team's point total from the previous season, and the away team's point total from the previous season, I could create a model that was as accurate as the bookmakers.

The question that remains is how much more accurate can the model become with the introduction of new variables? Beyond that, what variables should be used?

I am not sure I know the answers to those questions, but I am going to keep playing around with the data.