Example Excel Downloads
For the purpose of the example files, the data used is taken from the English Premier League, Season 2017-18 from the month of January, with Score Predictions all checked and all other filters left as default, although the dataset used is not critical.
***Important - Security Warning***
If you receive a security warning regarding macros being enabled when opening or using our files please see the Microsoft link here advising why you might get this, and how to allow the files to work: Internet macros blocked
Essentially you need to tick the unblock box as shown below. To do this, when your file has downloaded, right click on the file, select properties and general to get the options as shown below.
File: Statistical Relevance
Explanation of example: This file automatically calculates your P value and Archie score for any data you want to back test. You simply have to enter 4 elements into the file 1) Number of bets or selections, 2) Number of winners, 3) Yield or ROI, 4) Average odds across your selections.
There is a graphical display showing the P Value plotted over 2,500 simulated selections following the same win rate and yield as you have to date. Both the Archie Score and P Value have explanatory text within the file explaining what they do, and you can check out our YouTube channel here to find a walk through video example HERE
File: FBD Poisson prediction
Explanation of example: This Excel file is pre-loaded with the necessary Poisson Distribution calculations built in. You simply need to choose your league from our fixtures and results page, export the season of choice and paste into this example file. The spreadsheet will do the rest. You can select the two teams to compare by using the drop down filters where the team names are shown in yellow. This will allow you to check the mathematical probability of a number of first half and second half markets using Poisson.
Our own unique score predictions and odds projections that are available from our data archive or our daily sheets have another layer of complexion to them but the Poisson distribution is a good reliable base for you to be able to predict result outcomes. Of course, you always need to factor in situations such as travel, injuries, suspensions and weather etc when using any data based selection method.
Please note, this file will only work with Excel versions 2010 onwards due to the included Poisson function.
File: FBD 1st Goal
Explanation of example: This download shows you how you can quickly find the times of the first goal. Column BU shows the formula to find the time of the first home goal (where no goal is scored this is left blank). Column BV shows the formula for the first away goal time, and then column BW shows the time of the first goal scored in the match.
The simple pivot chart and graph show the count of first goals scored within 10 minute brackets. Any goal scored in added on time falls within the 90 minute bracket. Goals scored in the 9th minute and 1 second would count as the 10th Minute in terms of publishing and therefore under the bracket 10.
File: FBD Last Goal
Explanation of example: This download shows you how you can quickly find the times of the last goal. Column BU removes any "+1, +2, +3" etc from the official goal time of the home team where a goal has been scored in injury time. This accounts for up to 12 minutes of injury time, if you want to extend this please amend the formula further out than 12 minutes. Column BV removes any "+1, +2, +3" etc from the official goal time of the away team where a goal has been scored in injury time. This accounts for up to 12 minutes of injury time, if you want to extend this please amend the formula further out than 12 minutes. Column BW shows the time of the last goal scored by the home team in the game, and column BX shows the last goal time of the away side. Column BY therefore shows the overall last goal time.
The simple pivot chart and graph show the count of the last goal scored within 10 minute brackets. Any goal scored in added on time falls within the 90 minute bracket. Goals scored in the 89th minute and 1 second would count as the 90th minute in terms of publishing and therefore under the bracket 90.
File: FBD Last 10
Explanation of example: This example download shows the amount of goals scored in the last 10 minutes of games (including injury time). Columns BV:CD show the formula used to count the amount of goals scored in each minute listed in row 1. Column CE sums the amount of goals scored in the 10 minute period.
The basic pivot table and pie chart show the breakdown of games where goals have or haven't been scored in the last 10 minutes.
File: Over 2.5
Explanation of example: a basic formula to calculate games where the predicted number of goals in the game is more than 2, and the actual average over 2.5 odds is greater than the predicted over 2.5 odds. The formula then works out the profit or loss if you had back each qualifying game for a 10 pint level stake. In this example you can see only 2 matches meet the criteria - Everton vs Manchester United and Huddersfield vs West Ham. Over profit is 3.8 points.
File: Goal Sequence
Explanation of example: File will show how to display the sequence of goals scored in a game using our goal times column. Firstly the goal times columns (Home and Away) are copied into Column BU and BT respectively. We then use Excel's "Text to Columns" function to split out the goal times by minute. We allow 12 columns for each team as it is extremely unlikely a single team will score more than 12 goals in a competitive league fixture.
Then in columns CS:DG (shaded green) the sequence of goals are displayed. Where there are 2 goals scored in the same minute there is no way to differentiate so the formula skips this and doesn't favour either home or away side without having the knowledge of exactly who scored first.
This can help assess how often teams, or certain leagues see teams come from behind to win or draw, and by adding additional formula yourself you can expand on this further.
This is just a demonstration as to how you can use the data to test and create systems and is no means meant as a way to guarantee a long term profit.