Blog - Page 2 of 45 - Kind of Lost

How Full Should You Fill Your Tax Bracket? I Ran the Numbers

“Fill up your tax bracket” skips the hard part: which bracket. I ran my Roth conversion optimizer 34 times to see where it stops, and why a big IRA should fill more than feels comfortable.

“Fill up your tax bracket” is the most common Roth conversion advice there is, and it skips the only part that’s hard: which bracket? The 12%? The 22%? Halfway into the 24% because it’s only two points more? I got tired of the hand-waving, so I ran my own Roth conversion optimizer 34 times, changing one thing at a time, and watched where it stopped.

Short version: it almost always stops exactly at a bracket edge, never somewhere in the middle. Which edge depends on a single comparison, and that comparison isn’t the one most people make.

Chart: total Roth conversions by assumed future tax rate. With a $1.2M IRA it is flat at about $196k up to 20%, then jumps to $565k at 23% and above. With a $3M IRA it rises steadily and converts the whole IRA from 25% up. — Same retiree, same accounts, only the future tax rate changes. The small IRA doesn’t slide gradually, it jumps from one bracket edge to the next.

Airline Miles Are Worth What You’d Actually Have Paid

Back in 2018 I wrote a post ranking seven levels of credit card usage, and somewhere in the middle I said I prefer airline miles. I was very proud of the pun that followed (your miles may vary). What that post did not have was a single number to explain why.

So this is the number. More precisely, it is the argument that the number everybody quotes, what are airline miles worth in cents per mile, is the wrong one to look at. A mile is worth what you would actually have paid for the thing you used it on, minus what you still paid, compared against what the same spending would have earned in plain cash. That’s less exciting than a points-valuation chart, and it gives a smaller answer most of the time.

Vegas Lines and Injury Reports in My Lineup Optimizers

My free lineup optimizers for MLB, NFL and NHL now pull two things you used to have to look up in another tab: the league’s injury report and the Vegas lines. The part I care about most is how the Vegas lines get used, because the first version of that rule quietly did the opposite of what I built it for. Implied team totals are the most useful number on a DFS slate that isn’t in the salary file, so this is the post about how the tool gets them, what it does with them, and the one rule I had to throw away.

Strip chart: QB game totals under three rule settings; the first Vegas rule put 0 of 10 stacks in 50+ games, the shipped rule put 9 of 10 in 47+ games — Ten NFL lineups per row, built on a FanDuel slate with test Vegas lines. My first rule made the solver dodge high-total quarterbacks; the one I shipped does the opposite.

The MLB Lineup Optimizer Now Pulls Live Batting Orders

The MLB lineup optimizer just got an upgrade that matters more than any of the lineup rules it already enforced: it now knows who’s actually playing. Confirmed batting orders and probable starting pitchers load automatically from Major League Baseball’s own schedule data, and two new lineup filters use that information to keep bench bats and non-starting arms out of your build by default.

Until now, the tool would happily stack a hitter who wasn’t in that day’s starting nine, or a pitcher who was penciled in for Thursday, not today. That’s on the player to catch by cross-checking DraftKings against a lineup site. Not anymore — the optimizer pulls the day’s confirmed lineups itself and marks every player accordingly, the same way the NHL version of this tool already does with starting goalies.

Bar chart: 201 pitchers in the DK player pool versus 12 actually starting that day — On a real 6-game DK slate, 201 pitchers showed up in the player pool — only 12 were actually starting.

NBA DFS Variance Without a Subscription

The NBA optimizer is taking shape, and the shape is strange: the stacking panel is empty, the position rules are the site’s own, and the one feature I actually care about is a number nobody else gives away for free. This is where NBA DFS variance comes in, and why it stopped being something you have to pay for.

Short version of the last three posts: basketball creates no linked scoring unit, so there is nothing to stack; I measured that across three seasons and the correlations are about an order of magnitude weaker than football’s; and the old Excel model I dug up had already reached that conclusion by deleting the columns. What survived all of that is one idea, and it came out of the spreadsheet.

Scatter plot of 3,302 NBA teammate pairs showing that players who split minutes score against each other