MLB Bet Tracking Spreadsheet: Monitor Closing Line Value

Essential Spreadsheet Columns: Tracking Seasonal ROI and CLV

I have seen dozens of bet logs over the years. Most of them are the same: date, team, stake, win or lose, running total. That is not a bet log. That is a balance sheet. It tells you whether you are up or down but nothing useful about why. A real bet log is the diagnostic instrument that separates a punter who improves season over season from one who keeps making the same mistakes for ten years.

The MLB regular season generates 2,430 games. If you are placing two or three bets a day across that calendar, you will end the season with somewhere between 400 and 700 entries in your spreadsheet. That is enough volume for patterns to emerge – but only if you captured the right information at the moment of betting. You cannot reconstruct your reasoning three months later. You cannot remember whether the lineup was confirmed when you placed the bet. You cannot recall the weather forecast you saw. The data has to be captured live or it does not exist.

The good news is that the spreadsheet is genuinely simple. Eight columns plus a notes field, recorded inside two minutes per bet. Anyone with Excel or Google Sheets can build this in an evening. The bad news is that nobody else can do the work for you – every bet logged late, every column skipped, every cell with “tbc” in it that you never came back to fill in, eats away at the value of the log. Tracking only works if it is complete.

The eight columns you cannot omit

Every viable MLB bet log has the same skeleton. The names of the columns vary; the substance does not.

One: date and time of bet placement, not the game time. The gap between placement and first pitch tells you how often you are betting early prices versus closing prices, which matters for line-movement analysis later.

Two: the matchup, written in a consistent format. I use “AWAY @ HOME” – visiting team first, host team after the @ – because that is the format every MLB broadcast and box score uses. Pick one convention and stick to it. Mixed formats break your filtering forever.

Three: the market and the side, written explicitly. Not “Yankees”. “Yankees ML” or “Yankees -1.5 RL” or “Game total Over 8.5”. The market type has to be searchable later when you want to slice your performance by market.

Four: decimal price taken. Decimal, always. If your bookmaker shows fractional or American, convert before you log. Mixed formats in a single column makes summary maths impossible.

Five: stake, expressed in units rather than pounds. So “1.0u” or “1.5u” or “0.5u”. Recording pounds works on a flat bankroll, but if your unit size changes monthly or your bankroll grows, the units column lets you compare bets across the whole season on equal terms.

Six: result, written as “W”, “L”, “Push”, or “Cancelled”. MLB has plenty of edge cases – postponements, rain shortenings, suspended games settled to a specific inning – and your spreadsheet needs to handle each cleanly. A vague “lost” entry when the game was actually cancelled and your stake refunded breaks your ROI calculation silently.

Seven: profit or loss in units. For a 1-unit stake at 1.91 decimal that wins, profit is +0.91u. For the same stake that loses, profit is -1.0u. This is the column you sum to get cumulative profit and divide by total stakes to get ROI.

Eight: the closing decimal price. Captured separately from the price taken. This is the single most important column for diagnostic purposes – more important than the win-loss column, more important than the profit column, more important than every other field except possibly the stake size.

Why the closing-line column does more than your win-rate column

The closing line is the price the market settled on at the moment of first pitch – the most informed version of the price your bet existed at. If you took 1.83 and the close was 1.74, the market moved against the side you backed. That means you got a better price than the final consensus. Statistical research over decades has found this single metric, accumulated over a few hundred bets, is more predictive of long-term profitability than the actual win-loss record.

The reason is variance. Across 2,430 MLB games per season, the noise around individual outcomes is enormous. A punter with a genuine 55% win rate can easily run at 48% for two months. A punter with no real edge can run at 58% for the same period. Win rate, on a short sample, lies. Closing line value, on the same sample, tells the truth.

Capturing closing prices needs a small ritual. Either you check the price five minutes before first pitch and log it manually, or you take a screenshot of the bookmaker’s page just before kickoff and log it later that evening. Both work. The discipline is checking every bet, every time. Skipping the closing price column on losing bets is the most common form of self-deception in bet tracking – you start avoiding the painful data, and pretty soon your CLV column is mostly winning bets, which makes you look much sharper than you actually are.

Once you have a hundred bets logged with closing prices, do this exercise: count how many times the close was lower than your taken price (positive CLV) and how many times it was higher (negative CLV). For a profitable punter the ratio should sit above 55-45 in favour of positive. For a break-even punter it should sit around 50-50. For a losing punter, the ratio inverts. The result is brutally honest and arrives before your win-rate data has stopped lying.

MLB-specific fields: pitcher, lineup status, weather flag

Three extra columns are specific to baseball and they pay back the effort within a few months. Pitcher confirmation status, lineup confirmation status, and a weather flag.

The pitcher status column captures whether both starting pitchers were officially confirmed at the time you placed the bet. MLB starters get scratched fairly often – sore back, family emergency, last-minute manager decision – and the line on the game can move dramatically when the actual starter changes. Logging “both confirmed” or “away pitcher TBD” lets you analyse whether your early-price edges are real or whether they are just collected on bets that the line had not yet finalised.

The lineup status column does the same job. Confirmed lineups arrive about ninety minutes before first pitch. Betting before then means you are guessing at the batting order, which matters for player props and for moneyline assumptions about a hitter being benched. A column with “both lineups confirmed” / “home lineup pending” / “no lineups out” lets you segment your bets and see which timing window your edge actually lives in.

The weather flag is a single-character column for outdoor games. I use “G” for good, “W” for wind affecting totals, “R” for rain risk, “C” for cold. The flag captures whether weather was a factor in your bet selection. Six months later you can filter for “all bets where I logged W” and see whether your weather-driven plays actually beat the close. If they do, that is a confirmed edge worth scaling. If they do not, weather is something you should stop letting drive selections.

Building monthly and seasonal rollups

Eight columns plus three MLB fields is a lot of data once you have a few months of bets in the sheet. The rollups are what convert that data into something you can actually look at and improve from.

The monthly rollup is a single row per calendar month with seven numbers: total bets placed, total units staked, total units profit, ROI (profit divided by stakes), win rate, CLV win rate (the percentage of bets where the close was lower than your taken price), and average closing line edge in basis points. The closing line edge metric is decimal price taken minus decimal closing price, divided by closing price, expressed as a percentage. If you took 1.83 and the close was 1.74, your edge on that single bet is (1.83 – 1.74) ÷ 1.74 = 5.2%. Average that across all bets in the month and you have a stable number that gives you a real read on selection quality.

The seasonal rollup is the same numbers across the whole regular season. Some of the most useful information lives in the comparison between months. A punter whose CLV edge averaged +1.8% in April but dropped to -0.3% in July is doing something wrong – losing patience, chasing losses, betting markets they have not done work on. The pattern shows up in the rollup before it shows up in the bankroll, which is exactly when you need to see it.

I also build a market-segmented rollup once per quarter. Same metrics, but grouped by market type – moneylines, run lines, totals, player props, first-five innings. This is where you discover that, say, your run line bets carry +2.3% CLV edge and your strikeout prop bets carry -1.1%. The corollary is obvious: drop the strikeout props or restructure how you select them. Across an MLB season you do not have time to be sentimental about market types you keep losing on.

How the log earns its keep across a season

The first month of bet logging feels like overhead. The hundredth month feels like the most valuable habit you ever built. Once the sheet has eighteen months of bets in it, you stop having to guess at your own strengths. You can answer “do I actually beat the close on home dogs?” or “do I lose value when I bet before confirmed lineups?” with data. You can spot the patterns that variance is hiding from your eyes. You can size into markets where you have proven edge and away from markets where you have not. The log is also what makes your park-factor reads on totals testable rather than theoretical, because the historical evidence sits in your own data rather than someone else’s anecdote. Eighteen months of disciplined tracking is worth more than ten years of casual betting, and the spreadsheet is the only thing standing between the two.