Forensic Accounting: Analysis of AFL Matches Using Excel Spreadsheet

This document provides an analysis of Australian Football League (AFL) matches since 1897 using Excel spreadsheet to identify possible risk betting fraud. The process used to identify data changes and five specific examples of data changes identified in Excel spreadsheet are discussed. The finding indicates that there are a number of matches in AFL since 1992 where data has been changed for betting purposes. The observation of the finding suggests that the risk of betting fraud is much worse than primarily suspected at the beginning.

Running head: FORENSIC ACCOUNTING
Forensic Accounting
Name of the Student:
Name of the University:
Authors Note:

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Contents
Introduction:....................................................................................................................................2
5 specific examples of data changes identified in excel spreadsheet:.............................................2
Process used to identify the above examples:..................................................................................3
Finding:............................................................................................................................................6
Observation of the finding:..............................................................................................................7
References:......................................................................................................................................8

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Introduction:
Despite development of number of advanced accounting tools including customized
software the popularity of excel spreadsheet is yet undiminished. In fact the use of spreadsheet is
present in across the globe and experts prefer using spreadsheet to conduct data analysis. In this
document the data of Australian Football League (AFL) matches since 1897 have been analysed
by using different useful functions of excel spreadsheet to make certain assertions in regards to
these data. The matches played from 1897 till 2018 are to be investigated for possible risk
betting fraud. Considering the huge size of total number of data effective use of excel
spreadsheet would be immensely beneficial to identify changes in the data of these matches
(Abers and Hacker, 2016).
5 specific examples of data changes identified in excel spreadsheet:
Using certain important functions of excel, number of matches have been identified
where data has been changed in the file of the betting ringleader from the original data. Out of
those matches here are five such matches of AFL where data has been changed.
Date Roun
d
Team 1 Score Score Team 2 Score Score Venue
13-9-1992 SF West
Coast
20.13.133 17.15.117 Geelong 14.11.95 12.7.79 M.C.G.
26-9-1992 GF West
Coast
16.17.113 20.19.139 Geelong 12.13.85 13.8.86 M.C.G.
7-9-2008 QF Geelong 17.17.119 9.13.67 St Kilda 8.13.61 19.8.122 M.C.G.

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20-9-2008 PF Hawthorn 18.10.118 14.13.97 St Kilda 9.10.64 16.16.112 M.C.G.
1-7-2018 R15 Melbourne 18.9.117 8.13.61 St Kilda 18.11.119 16.11.107 M.C.G.
The blue highlighted part shows the dates of different games where the data has been changed.
The red highlighted scores are the manipulated score found in the laptop of betting ringleader
with green colour highlighting the original score of these matches (Harris et. al. 2016). The
above five examples show how the betting fraud is a reality as is visible in the above example
table.
Process used to identify the above examples:
The step by step process used to conduct the investigation on AFL matches are provided
below:
Data collection:
At the very beginning, the original data of AFL matches were collected from the official website
of AFL. The data of each matches since 1897 in chronological order was exported in an excel
file. Also the file recovered from the laptop of arrested betting man was kept in right condition as
it was recovered without any changes (Christensen and Schneider, 2017).
Merging of data in a single file:
The original data of AFL matches were merged with the data maintained in the arrested betting
man’s laptop. In order to merge the data firstly an additional column each was inserted right

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beside the score column in betting man’s file. One column each was inserted for team 1 score
and team 2 score.
The following image would be helpful in understanding the process used to merge the data:
F and G columns and J and K columns were inserted in the betting man’s file to conduct data
analysis on the AFL matches (Steinbach et. al. 2015).
Sorting and filtering:
After insertion of separate columns as shown in the above image, sort and filter function of excel
were used to sort and filter data according to requirements of the investigator. The image below
depicts where the sort and filter function is located and how to use the function (McFedries,
2016)
This function helped in sorting and filtering data on the basis of date, team 1, team 2, score and
venues. Thus, as per the requirement of the investigator data can be sorted and filtered using any
or more of the above data classifications.

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“IF” function and use for identification of games where data has been changed:
“IF” function is an important function of excel spreadsheet which helps in conducting data
analysis on the basis of certain logic. In this case the logic has been based on the score of the
AFL matches (Conant, 2018). The formula has been created in such a way to compare the scores
of AFL matches as recorded in betting man’s file with the original scores of these matches. The
following image shows the formula that has been used to compare the data of AFL matches.
As a result, in case the data in betting man’s file has been changed will be highlighted by FALSE
where as TRUE sign will indicate no changes in data. The screen shots below taken from the
excel file where the analysis has been conducted would help in identifying the matches where
data has not been changed (Li et. al. 2017).

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In contrast the following screen shots show the AFL matches where data has been changed as the
formula cell indicate the FALSE sign.

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Finding:
Finding from the investigation clearly indicates that there are number of matches in AFL
since 1992 where data has been changed for betting purposes. Here only five instances of such
matches have been provided where data has been changed however, the number of matches
where data has been changed is huge (Harkins, 2016).
Observation of the finding:
It is frightening to conclude that the betting fraud risk in AFL is much worse than
primarily suspected. The betting has been continuing since 1992 and is very much a part of the
annual sporting league even at the present. The examples have been specifically given to clearly
show that the betting has been in force since 1992 and is continuing even in the present. The
table below shows that from 1992 till now in 2018, the changes have been made to the original
score in the betting man’s file to manipulate the records for betting purposes.
Date Team 1 Score Score Team 2 Score Score

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13-9-
1992
West
Coast
20.13.133 17.15.117 FALSE Geelong 14.11.95 12.7.79 FALSE
26-9-
1992
West
Coast
16.17.113 20.19.139 FALSE Geelong 12.13.85 13.8.86 FALSE
7-9-2008 Geelong 17.17.119 9.13.67 FALSE St Kilda 8.13.61 19.8.122 FALSE
20-9-
2008
Hawthorn 18.10.118 14.13.97 FALSE St Kilda 9.10.64 16.16.112 FALSE
1-7-2018 Melbourne 18.9.117 8.13.61 FALSE St Kilda 18.11.119 16.11.107 FALSE
From the above it is clear that the risk of betting fraud is much worse than primarily suspected at
the beginning. This is not about one or two matches but a whole host of games of AFL where
unauthorized changes have been made to the original score as per the manipulative file recovered
from the excel file of the betting man. Thus, all necessary precautions shall be taken to ensure
there is no way betting fraud can impact the results of the AFL matches (Harris et. al. 2016).
References:
Abers, G.A. and Hacker, B.R., 2016. A MATLAB toolbox and Excel workbook for calculating
the densities, seismic wave speeds, and major element composition of minerals and rocks at
pressure and temperature. Geochemistry, Geophysics, Geosystems, 17(2), pp.616-624.

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Christensen, D. and Schneider, P., 2017. Allocating service department costs with excel: Excel's
iterative calculation option makes it easier to use the reciprocal method to allocate service
department costs. Strategic Finance, 98(11), pp.50-56.
Conant, D.D., 2018. Examining critical and near-critical paths: an Excel-based classroom
exercise. Journal of Education for Business, 93(6), pp.285-291. Available at:
https://www.tandfonline.com/doi/abs/10.1080/08832323.2018.1490685 [Accessed on 8 October
2018]
Harkins, S., 2016. Build your Excel skills with these 10 power tips.
Harris, A.J., Rhéty, M., Gurioli, L., Villeneuve, N. and Paris, R., 2016. Simulating the
thermorheological evolution of channel-contained lava: FLOWGO and its implementation in
EXCEL. Geological Society, London, Special Publications, 426(1), pp.313-336.
Li, Y.N., Wu, Q., Tang, W., Qin, B., Wang, Q. and Miao, M., 2017, December. Outsourcing
Encrypted Excel Files. In International Conference on Information Security Practice and
Experience (pp. 506-524). Springer, Cham.
McFedries, Paul. Excel 2016 formulas and functions. Que, 2016. Available at:
http://cds.cern.ch/record/2113549 [Accessed on 8 October 2018]
Steinbach, S.M., Sturgess, C.P., Dunning, M.D. and Neiger, R., 2015. Use of a Microsoft Excel
based add-in program to calculate plasma sinistrin clearance by a two-compartment model
analysis in dogs. Research in veterinary science, 100, pp.263-264.