ANDERS KARLSEN CEO, Sisu Group
TUDOR TAMAS Product Experience Director, Adjarabet
KARLO LEVAK COO, Sofascore
Quality over Quantity Making data accessible, flexible and usable
Enhanced by data analytics, sports betting experiences offer improved accuracy and deeper insights. Ahead of their panel discussion at SBC Summit, Tudor Tamas, Karlo Levak and Anders Karlsen explain the data-driven strategies available to anticipate player needs, optimise engagement, and drive innovation.
How do you extract quality from the increasingly vast amounts of data now available? What tools are available to make data accessible, flexible and usable?
Tudor: I’ll start from what we understand by quality in this context: it means the data is used to specific outcomes that drive real value to the business – value that is translated and measured through the growth of specific key results. With so much data available, one of the biggest challenges for organisations is understanding not only what they are looking at but also why: what are the questions they want to answer? Most times you can have the best tools at hand like Looker or Power BI (for BI), Python with Statsmodels as a library for stats analysis or Snowplow/Content Square for behavioural insights; but unless the business is clear on the outcomes they anticipate to obtain and delve in the data with very specific questions that help build the final picture, there’s a high chance data remains as just noise.
Karlo: At Sofascore, we leverage our internal AI department to handle and analyse the extensive data generated from our platform, which encompasses over 1 petabyte of football statistics and user interactions. Our team employs advanced analytics techniques to
74
extract meaningful insights from this vast data pool. We have developed in-house machine learning (ML) models that perform automated analyses, ensuring that the data is not only accessible but also flexible and highly usable. Tese models help us transform raw data into valuable content, enhancing the user experience by providing detailed, context-rich information about matches, player performance, and more. By continuously refining these tools and methodologies, we maintain the quality and relevance of the data presented to our users.
Anders: It's important to use different sources of data and ensure that the data extracted is quality assured. Tis ensures accuracy and the lowest possible latency of the odds offered, which is especially important for sports and leagues where the betting volume has low liquidity. We have developed our own models, including predictive odds models, risk models, and customer classification models. Tese are constantly fed with new real-time data to ensure they remain up to date. Underpinning this, is our in-house data platform which consolidates all data across player activity and enables us to deliver a curated product offering. Te data platform has been built in the cloud and having in-house expertise will enable the platform to scale
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
