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Overtime Markets Top Most Profitable Traders

Source : Twitter

In this article, we aim to identify the 5 most profitable traders on Overtime Markets in the last two weeks. How much has each wallet won by sport?

Introduction

Thales is a protocol that allows for the creation of peer-to-peer parimutuel markets that anyone can join. A Parimutuel Market is a market where the collateral is locked in a pool and the final payout to participants is not determined until after the market-settling event finalizes. There are several types of markets, including Up and Down markets, ranged markets, exotic markets, and Overtime Markets.

Overtime markets is a fully on-chain sports AMM with Chainlink sport feeds, allowing users to bet on Soccer, Football, MMA, and Baseball. Here in this dashboard we will be focused on the Overtime volume on Optimism.

How Do Overtime Markets Work?

Ultimately, Overtime Markets is the first permissionless AMM based on the Thales Protocol. Thales Protocol is an elegant and novel core design on which Overtime Markets is built.  On the Optimism network, Thales protocol is currently operating. As a fully on-chain sports AMM, Overtime markets offers Chainlink sport feeds that allow users to bet on Soccer, Football, MMA, and Baseball. The Thales markets have generated millions of dollars in volume through their immutable smart contracts, which are completely decentralized and  controlled.Through Overtime, communities can trade sports without relying on centralized entities for exchange of goods and services.  There are four stablecoins supported by Overtime: sUSD, USDT, USDC, and DAI.

With Overtime, you are not only bringing the game to blockchain, you are doing so with some of the best odds available across all sportsbooks, not just cryptocurrency ones. This odds are provided by Pinnacle Sportsbook, a highly regarded bookmaker that has a customer base in over 100 countries. In 1998, Pinnacle was founded and is considered one of the most reputable sportsbooks on the internet.By using Chainlink data feeds, Overtime receives these odds in real-time and uses them to price their positional tokens for each market.  There is one update for each game every six hours, except on game days when it is updated hourly.

Overtime’s Odds : In order to price their positional tokens for each market, Overtime receives these odds through Chainlink data feeds on-chain. Dollars of potential profit are priced based on implied win probability instead of percentages. Based on Pinnacle’s data, we can assume that the implied win probability of a Home win is 44% if it has a price of 0.44 per $1 of potential profit. There are different kinds of odds,

In what ways does the Sports AMM work?

There are three outcomes supported by the Sports AMM Smart Contracts: Home Win, Away Win, and Draw.There are three parts to the Sports AMM workflow:

Approach

In this dashboard, we will examine the top10 most profitable wallets using Overtime volume on Optimism, which is generated using contract_address in the last two weeks, and find out the volume of each wallet won by sport.

Volume On Overtime Markets For The Past 2 Weeks 

Here from the below graph we can see the Volume On Overtime Markets for the past 2 weeks . Here we can see the volume between August 11, 2022 and August 25, 2022 was 252.52K.  A maximum volume of 70.12K was observed on August 14, 2022, and a minimum volume of 2.01K was observed on August 16, 2022. There is a downward trend in the total volume. August 14, 2022 had a higher total volume than usual.

Here in this section , we can also see the volume by sport on Overtime markets for the past 2 weeks period. Between August 11, 2022 and August 25, 2022, total volume by sports was 252.52K. More than 80% of the total volume came from the top 3 sports (MLB, EPL, and NFL).  There is a low total Volume of 3.63K (MLS), and a high total Volume of 153.09K (MLB). The maximum Volume of 70.12K was observed on August 14, 2022, and the minimum Volume of 2.01K was observed on August 16, 2022. There is a decreasing trend in total volume. The total volume in August 14, 2022 is higher than normal. In comparison with the first week, 5 sports saw declines in volume, with the EPL dropping the most at more than 99%.

As can be seen from the below graph, the Top 3 Sports Baseball, English Premier League, and NFL accounted for 80.24% of total volume out of all games. Volumes are lowest in the Major League Soccer (MLS) and highest in the Major League Baseball (MLB). In comparison with Major League Soccer, MLB generates almost 40 times more volume.As you can see, the EPL generates the second highest amount of volume. In the following order is Serie A.

Volume And Distribution Of The Top 10 Players  Across All Sports

In this section, we can see the top ten players by volume across all sports as well as how many players won and lost bets over the past 14 days. We can also see the distribution of profits and losses by players, too. Based on the volume of the top 10 players, it is 76.44K. As a result, the top two players contributed 29.01% of the volume and the top four players contributed 52.73%. The lowest total Volume is 5.38K (0x4cc423d28183e05fbc5c70d1ae3424de4c4aacff) and the highest total Volume is 11.22K (0xf68d2c2ea6156e7faf7982335b7d0be57502b14a).

Here from the below graph we can see the out of all number of players more than 55% of the players were lost their bet . That means we can see more number of players are losing their bet amount .

In the past two weeks, there have been 696 players. According to the amount distribution of profit and loss by players over the last 14 days. It is estimated that 310 players lost their bet amount under the range of $17.97. On the other hand, 249 players earned a profit in the amount of $18.94. This means that out of all players, almost 559 are in the range of profit and loss below a 20-dollar bet. Rest of the players were in the other categories of losses and profits. We can also see that the maximum loss amount for players is $9873. In a bet of $9244, the player can make the maximum profit.

Top 10 Players Bet Volume: How Much Do They Win or Lose?

Here we can see the top 10 players bet volume and how much they won or lost and also what kind of game they got more profit from and loss from. For the past 14 days, the graph below shows the top 10 players who won their bets on which game.  The highest bet amount won in the past 14 days was $9244 by Boston Red Sox Vs New York Yankees game. On the second top game, a player won $6.13K while playing Cincinnati Reds vs Chicago Cubs. Among the top 10 players, different players won the wager amount on different games. We can therefore conclude that these winning players are not related to the type of game they played. 

Below is a graph showing the top 10 players who lost their bets on which games in the past 14 days.  Out of the top 10 players, 3 of them lost their bet amount while playing Chicago Cubs vs St. Louis Cardinals within the last 14 days.  In addition, we can see that the player who played Boston Red Sox Vs New York Yankees lost a maximum amount of money. 

Observations

Reference Query

with ctrs as (
select
     ADDRESS,
    SYMBOL,
    DECIMALS
  from
    optimism.core.dim_contracts
  where
    ADDRESS in (
      '0x7f5c764cbc14f9669b88837ca1490cca17c31607',
      '0x8c6f28f2f1a3c87f0f938b96d27520d9751ec8d9',
      '0xda10009cbd5d07dd0cecc66161fc93d7c9000da1',
      '0x94b008aa00579c1307b0ef2c499ad98a8ce58e58'
    )
),
tags as ( -- https://github.com/thales-markets/contracts/blob/67d12f1c549026ff6a8d1fdaac832ff427a17573/scripts/deployExoticMarkets/deploy_ExoticTags.js
  SELECT 'Sport' AS label, '1'as tag UNION 
SELECT 'Crypto' AS label, '2'as tag UNION 
SELECT 'Politics' AS label, '3'as tag UNION 
SELECT 'Pop-culture' AS label, '4'as tag UNION 
SELECT 'Esports' AS label, '5'as tag UNION 
SELECT 'Football' AS label, '101'as tag UNION 
SELECT 'Basketball' AS label, '102'as tag UNION 
SELECT 'Bitcoin' AS label, '201'as tag UNION 
SELECT 'Ethereum' AS label, '202'as tag UNION 
SELECT 'Finance' AS label, '6'as tag UNION 
SELECT 'TradFi' AS label, '601'as tag UNION 
SELECT 'NCAA Men''s Football' AS label, '9001'as tag UNION 
SELECT 'NFL' AS label, '9002'as tag UNION 
SELECT 'MLB' AS label, '9003'as tag UNION 
SELECT 'NBA' AS label, '9004'as tag UNION 
SELECT 'NCAA Men''s Basketball' AS label, '9005'as tag UNION 
SELECT 'NHL' AS label, '9006'as tag UNION 
SELECT 'WNBA' AS label, '9008'as tag UNION 
SELECT 'MLS' AS label, '9010'as tag UNION 
SELECT 'EPL' AS label, '9011'as tag UNION 
SELECT 'Ligue 1' AS label, '9012'as tag UNION 
SELECT 'Bundesliga' AS label, '9013'as tag UNION 
SELECT 'La Liga' AS label, '9014'as tag UNION 
SELECT 'Serie A' AS label, '9015'as tag UNION 
SELECT 'UEFA Champions League' AS label, '9016'as tag  
),
games as (
  SELECT 
    distinct regexp_substr_all(SUBSTR(data, 3, len(data)), '.{64}') AS segmented_data, 
    concat('0x', substr(segmented_data[0], 25, 40)) as game_address,
    trim(HEX_DECODE_STRING(segmented_data[13] :: STRING), char(0)) as home_team,
    trim(HEX_DECODE_STRING(segmented_data[15] :: STRING), char(0)) as away_team,
    concat (home_team, ' vs ', away_team) as game_name,
    ethereum.public.udf_hex_to_int(segmented_data[17] :: STRING) :: STRING as tag
  FROM optimism.core.fact_event_logs
  WHERE contract_address = '0x2b91c14ce9aa828ed124d12541452a017d8a2148'
    AND topics[0] = '0x889e2060e46779287c2fcbf489c195ef20f5b44a74e3dcb58d491ae073c1370f'
  	and EVENT_REMOVED = 'false'
),
game_tags as (
  select 
  	label as sport,
  	game_address,
  	tag,
  	game_name
  from games INNER JOIN tags using (tag)
),
txns as (
  	SELECT 
  		block_timestamp,
  		tx_hash,
  		origin_from_address as player,
  		RAW_AMOUNT / pow(10, DECIMALS) as amount_bet,
  		SYMBOL,
 		concat('0x', substr(regexp_substr_all(SUBSTR(input_data, 11, len(input_data)), '.{64}')[0], 25, 40)) AS game_address,
  		Sport,
  		game_name
  	FROM  optimism.core.fact_token_transfers trnf
  	INNER JOIN ctrs ON trnf.contract_address = ctrs.address
  	INNER JOIN optimism.core.fact_transactions as txn using (tx_hash)
  	INNER JOIN game_tags ON game_tags.game_address = concat('0x', substr(regexp_substr_all(SUBSTR(input_data, 11, len(input_data)), '.{64}')[0], 25, 40))
  	WHERE origin_from_address = from_address
  	and ORIGIN_TO_ADDRESS = '0x170a5714112daeff20e798b6e92e25b86ea603c1'
  	and block_timestamp >= CURRENT_DATE - {{days_ago}}
)
SELECT 
  	player,
  
  	sum(amount_bet) as volume_usd
FROM txns
GROUP BY player
QUALIFY row_number() over(ORDER BY volume_usd DESC) <= 10

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