Another thing that sets us apart is our obsession with data
quality. A lot of the industry still treats that as a secondary issue, but we see it as the foundation of everything else. We have created and optimised processes that clean, refine and validate data at scale - and that has given us a level of accuracy and confidence that is unmatched in the market.
The Bettormetrics Way Clean Data, Clear Decisions
In an industry that often treats data accuracy as an afterthought, Bettormetrics has made it the cornerstone of its analytics engine. By continuously perfecting its data cleaning and
validation processes, Sabin Brooks, Chief Revenue Officer, explains how Bettormetrics delivers insights that inform strategy and unlock new opportunities for innovation.
SABIN BROOKS Chief Revenue Officer Bettormetrics
Bettormetrics was established with the ambition to become the industry standard in sports trading analytics. How has the company's mission evolved since launching in 2022?
Tat original ambition has not changed but now we are in a far better position to understand how we achieve it, and what success looks like. Over the past three years we have learned a huge amount about the realities of sports trading data: the challenges of surfacing meaningful insight from complex and inconsistent sources, the dynamics of the aggregator landscape and the nuances of key operator use cases.
We have also significantly boosted our engineering and human capabilities around big data and increasingly taken a more visible role in externalising our authority around data. Trough industry reports and benchmarking initiatives, we have aimed to bring greater transparency and accountability to trading performance where it is often been lacking.
So, while the mission remains the same, our understanding and execution of it have become far more sophisticated.
What unique value propositions does Bettormetrics offer to differentiate itself from other players in the market?
90
Our biggest differentiator is the depth and quality of the data we work with, and the expertise that we have built around it. Bettormetrics has access to more data than most companies in the industry, but the real advantage comes from what we do with it. Over the past few years, we have developed powerful visualisation tools and manipulation processes that allow us to extract meaningful insights from incredibly complex feeds. Another thing that sets us apart is our obsession with data quality.
A lot of the industry still treats that as a secondary issue, but we see it as the foundation of everything else. We have created and optimised processes that clean, refine and validate data at scale - and that has given us a level of accuracy and confidence that is unmatched in the market.
Tat said, we continue to invest heavily in improving these processes because we believe it is a worthwhile effort, and one that will allow us to pivot into new areas where sportsbooks really need deeper analytical support and the integrity of reliable data sources.
How is Bettormetrics leveraging big data technologies? We deal with terabytes of data every day, so scalability and efficiency
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164 |
Page 165 |
Page 166 |
Page 167 |
Page 168 |
Page 169 |
Page 170 |
Page 171 |
Page 172 |
Page 173 |
Page 174 |
Page 175 |
Page 176 |
Page 177 |
Page 178 |
Page 179 |
Page 180 |
Page 181 |
Page 182 |
Page 183 |
Page 184 |
Page 185 |
Page 186 |
Page 187 |
Page 188 |
Page 189 |
Page 190 |
Page 191 |
Page 192 |
Page 193 |
Page 194
