Saturday, June 18, 2011

Update/correction to risky site model: Casualty losses and tax effects

This is not the kind of stuff that I want to be spending my time writing about, but I discovered a serious enough practical change to one of my old analyses that I felt it was important to inform you all of it.

In my model for bankroll management and game selection in risky sites, I made the following assumption, which I thought was a reasonable guess as to the tax implications of losing access to poker funds:

...For simplicity, we'll assume that any money lost due to site-specific risks [sites closing down or withdrawals being seized] is treated the same as a poker loss and directly deductible against poker winnings, though I believe this is not exactly true.

It turns out that this is indeed not exactly true. In fact, being able to deduct this money against poker winnings is probably not possible in many cases.

The default classification for such a loss would be as a casualty loss. Even though a player may have been "gambling" on the chance that the site would return his funds, this must be considered a casualty loss rather than a gambling loss, which brings about some serious restrictions in many cases. The effects on the affected player's bottom line are significant.

Tax effects of stolen funds for those who file as professionals

Professional poker players can take such a casualty loss related to his poker business without any limitations. So, if you file as a pro, you can ignore all of this.

The original approach is still accurate for anyone filing as a professional on their taxes.

...but for amateur players...

Amateur players most likely have to take the loss of poker site funds as a casualty loss, which is a deduction that is strictly limited. A casualty loss can only be deducted to the extent that it exceeds 10% of adjusted gross income (AGI) — and, remember, while an amateur player still ends up getting to use his net (positive) gambling winnings for his bottom-line taxes, the AGI is a figure which is calculated after all winning sessions are added, but before losing sessions are deducted.

Amateur players can only take a casualty loss if it exceeds 10% of their AGI, an amount which will always be artificially inflated above actual income due to session-by-session accounting that amateurs must use.

In almost all practical cases, this will mean that losses due to the risks of the current online poker environment will not be deductible at all unless you file as a professional.

Bob, a formerly-winning recreational small-stakes player, decides to deposit $500 on a current risky U.S.-facing poker site. Though he knows he will not make much money, he enjoys playing and wants to keep his poker skills in practice. He averages $3/hr in risky site funds by playing on the site, and after playing for several months, he has run his balance up from $500 to $1,500. The site then has its funds seized, declares bankruptcy, or otherwise absconds with the money. Bob was never able to cash out successfully.

The results:
  • If Bob's AGI for the year is above $15,000, he cannot deduct any of the $1,500 that he lost. This is almost certainly the case, even if Bob does not suffer from undue AGI inflation from his poker sessions.
  • However, Bob still earned $1,000 in gambling winnings from his play on the site, even though he never got his money.
  • Notice that, given that the site ended up disappearing, Bob would have been better off if he lost money on the site. Winning has increased his taxable income, despite not providing any actual money or deductions to Bob.
Effectively losing money by winning at poker on a site that disappears is highly unfortunate, and a pretty big deterrent to playing at all when there is any risk of losing one's online balance to the site-specific risks.

Implications for the model

I modified the old model by making the losses due to unretrievable funds occur after taxes, rather than before.

To keep the charts simple, I considered only the 50NL case this time. See the old article for the other assumptions.

We see that, as one might expect, the effects of this tax correction do not change much based on the per-day probability of site closure (i.e. the average lifetime of the site). If the site is likely to die at some point, the hit of this tax situation will be about the same whenever it happens, at least when cashouts are liquid enough along the way.

Much more important is the liquidity of the site, which will dictate the probability of funds being lost forever when the site disappears. After all, these funds being lost is what causes this fun little tax situation to occur.

Once the probability of losing funds becomes reasonably high, the utility gained by playing drops off quite a bit. Remember, playing and winning will go on to cost money if the site ends up not paying. Even in the extreme case where there is no probability of the site ever allowing a cashout, the "money" won on that site would still be considered gambling winnings for a player who knowingly put money onto the site for the purposes of practicing his game. What a mess.

Overall, I would think that any reasonable estimate of the safety of current risky sites is going to involve more than enough risk to really cut into the expected utility of playing. So, if nothing else, this provides another strong incentive to choose even lower stakes than one otherwise might. The less funds that are at risk, and the less likely a player is to win money and go on to get it stolen from him, the less the effects of these negative tax implications will be.

Other bad news

Americans who don't file as professionals and who currently have funds stuck on Full Tilt Poker (or, more likely, the other, less-reputable sites) will also suffer negative tax effects if those funds end up not being returned to the players. Unless their online bankrolls were more than 10% of their total income for the year (plus the phantom session-by-session income in the AGI), recreational players will get no deductions at all if their money is absconded with.

Strangely, in the event that the funds aren't returned, winning recreational players would have been much better off somehow losing their entire bankrolls prior to April 15.

In particular, if Full Tilt Poker goes on to end up not returning U.S. funds, then of the alleged $150 million in American funds that are stuck there, I would guess that at least $50 million of them will be completely non-deductible, even though much of that sum represents taxable poker winnings.

Possible exceptions

It's possible that, at least in the case of money put on post-Black Friday sites knowing that there would be a chance of not being able to get money out, there might be a way to argue that the losses should be gambling losses rather than casualty losses.

The key factor here is the notion of constructive receipt, which is the rule which causes poker income to be taxable when it is earned rather than when it is cashed out. The underlying principle is that, as soon as a taxpayer is able to undergo actions to have the money in their hand, that income is immediately taxable.

In the case of a poker site that disappears with player funds, one might argue that the player did not actually have the ability to ever receive the money and thus that constructive receipt does not apply. If this approach were deemed to be valid, there would be no taxable income.

It is likely important that the funds weren't accessible at the time they were won, rather than just at some later date when the site closed or when a cashout was attempted. For a hypothetical site which has never processed withdrawals, this would be true. For U.S.-facing sites which are currently not processing U.S. withdrawals, perhaps this is true, though the mere possibility of you being able to successfully withdraw in the time after the money was won might be enough to invalidate this. For pre-Black Friday sites, it's definitely less true, as though it certainly had been difficult to withdraw funds prior to Black Friday, people were able to do it with some degree of regularity. It's hard to guess where this line would be drawn.

Also, it is difficult to find a way to reconcile this sort of accounting rule with the necessary session-by-session accounting, which implicitly assumes constructive receipt.

I find the tax effect described in this article to be quite absurd, even in the context of the other IRS rules that produce various unfair tax situations for poker players. Despite this, there might not be any reasonable way around it. Filing one's taxes as if these losses were gambling losses instead of casualty losses may not be considered appropriate by the IRS, which has a history of interpreting rules for anything related to "gambling" as harshly as they are able to. Fighting to clarify the nature of these losses may be costly.

Thanks to taxdood, Russ Fox, and PokerXanadu for helping me understand this tax situation.

Wednesday, June 8, 2011

WSOP Utility Analysis revisited, part 2: How many shares should a WSOP Main Event player sell off?

Last time, we looked at the relationship between a player's expected utility in the 2010 World Series of Poker Main Event and his skill advantage over the field. Under a particular proposed shape of finish probability distribution, we found that a raw ROI of about 86% (i.e. an average cash of $18,600) was necessary for a player with a typical income, risk aversion, and tax obligation to simply break even in terms of expected utility. Even in a juicy WSOP Main Event field, this is a pretty lofty goal for most, and many near-average players will be forced out of participating on their own dime unless they are willing to effectively pay for the privilege.

Fortunately, backing and staking agreements are common for large-field poker tournaments. Much as the stock market investor would never put a large portion of his capital into a single investment unless it were extraordinarily profitable, the poker player (who "invests in himself" in his poker career) will often benefit from diversifying away some of his risk by hedging his tournament results out to others. If these mediocre winning players players are able to find other parties to put up part of their entry fee in exchange for part of their prize, they will be able to yield a positive expected utility in the event, not only for themselves, but also for their investors.

Shares sold at face value

For the purposes of this analysis, we assume that only option of staking/backing available to the player is to sell off X% of his prize in exchange for X% of the cost of entry (selling shares at 1-to-1, with no markup). The result for the player of such a contract will be the same as if the entire tournament were scaled down by X%. A player can sell off 90% of himself to effectively make the WSOP Main Event a $1,000 buyin tournament for him, with prizes which are exactly proportional to those of the true main event.

For the time being, we are ignoring some other popular forms of staking and backing (listed in the order of likelihood that I might add them to the model in the future):
  • Selling shares at a price other than 100% of face value — If a player with a significant skill edge wanted to sell pieces of his action, in reality, he sould expect to get much better than 1-to-1 from his investors, since he's the one doing the work. The investors would still be left with a very profitable, fast, hands-free investment. Conversely, a -EV player might still be able to gain some expected utility by selling pieces of himself at a discount.
  • Direct backing — One popular form of contract is for the investor to provide all of the player's entry fee in exchange for a payoff equal to a fixed percentage of the player's profit in the event that he cashes. This is a freeroll for the player and will thus always yield him a positive expected utility, and it can still provide the investor with a positive expected utility as well if the player is sufficiently skilled. It is reasonable to expect that this sort of deal may be more favorable than selling shares at face value if the player is very skilled, but also very risk-averse relative to the stakes of the event.
  • Long-term, ongoing backing agreements — Some players have professional backers with whom they enter into long-term deals. The investor pays all of the player's buyins for a series of tournaments in exchange for a percentage of the player's profits, but if the player is already at a net negative from previous tournaments, he must repay that amount to the backer in full before being able to realize any profits from the contract. These agreements have several variables and would be complicated to analyze, and players under such agreements may not have the opportunity to consider other hedging options anyway, since they are often locked into their contracts until they expire.
For now, we consider only the simplest case: shares sold at face value.

Given this opportunity to rescale the stakes of the tournament, assuming that there is an investor willing to buy any amount of shares that the player would offer, how much should the player look to sell off?

Optimizing share-selling for the typical player

Our typical, risk-averse player ($80k net worth, $50k income with at least $10k from poker, risk aversion of 0.8) will realize the following expected utilities based on his skill advantage and the percent of himself he chooses to retain:

Here, the different colored lines represent different several different levels of skill edge, expressed in terms of raw ROI. The horizontal axis shows the percentage of his own action that the player takes; the amount he sells off is equal to 100% minus this number.

In red — For the player of precisely average skill, who has a raw ROI of -6% (due to rake), we see that, regardless of the number of shares he sells, he cannot realize a profitable opportunity in this event. Since he's a break-even player and is risk-averse and experiences tax effects that are negative on average, he's going to lose utility by playing any poker tournament, regardless of how small he makes the stakes.

In orange — When he was forced to take all of his own action, we recall that the small winner (raw ROI of 50%) was forced out of being able to profit from his small skill advantage at all. We showed that the minimum ROI required for a positive expected utility is 86%, and the chart verifies that if this 50% ROI player were to take all of his own action, he would be losing money after taxes and risk aversion. By selling shares, we see that he can realize a small positive expected utility ($121 in certainty equivalent) by playing for about 12% of his own action. The ability to hedge against the entry fee has allowed the skilled, risk-averse player to realize a profitable opportunity where he otherwise could not.

In yellow — This solid winning player (raw ROI of 100%) is a strong enough player that he will realize a positive expected utility even if he takes all of his own action, as we can see by the yellow curve being completely above the x-axis. However, we see that this player will realize an even higher expected utility by selling off some of his action than by paying his own way entirely. He'll improve his certainty equivalent payoff from $532 to $805 by selling off roughly half of his action.

In green — A bigger winner (raw ROI of 150%) turns out to do best by playing for all 100% of his own action. The investment has become so profitable that even a risk-averse individual does best by taking it all on and not hedging it out to others.

In blue — This big winner (raw ROI of 200%) has similar results to the green player above.

Other cases: different risk/tax profiles

If we keep the player's relative risk aversion fixed at ρ=0.8 but increase his wealth from $80k to $500k and his annual income from $50k to $100k, he becomes more able to handle his own risk:

The breakeven player is, of course, still unable to profit, and it turns out the 50% ROI player still benefits slightly from selling off some of his action, but overall, the additional risk tolerance incentivizes this player to hold onto all or most of his own action.

Alternatively, instead of adjusting wealth, we can reduce the player's relative risk aversion from ρ=0.8 to ρ=0.5, representing an individual who is more willing to take on risk (at least for the special occasion of the WSOP Main Event, perhaps):

The nature of the effects is similar. It is worthy to note that the orange curve (raw ROI of 50%) is similar to that of the original analysis for the more risk-averse player, suggesting that a small winner with an average wealth should still be selling off most of his action regardless of his personal preferences for risk. On the other hand, it looks like the yellow curve (raw ROI of 100%) has become roughly the point where the player will prefer to take 100% of his own action for this particular level of risk aversion, so players with significant skill edges should be more inclined to take all of their own action if they have a higher tolerance for risk.

Optimal hedging percentages

Thanks to the complexity of the utility function and the sheer number of different payoffs, there is no simple way to express a formula for the curves we've found above. In order to calculate the optimal hedging percentages (i.e. the percentages of action to take which correspond to the maximum points of these curves), we proceed numerically.

Here, we disregard the ρ=0.5 case treated directly above and consider only the first two cases: the original "typical player" (in yellow below) and the "wealthy player" (in green below):

(Ignore the jaggedness of these curves; the negligible inconsistencies are a consequence of the numerical error of Excel's goal seek solver.)

Rather than only considering five different specific values, this chart looks at every possible value of raw ROI and provides a more comprehensive practical resource.

As we've seen earlier, a player with an ROI less than 0% does best by playing for 0% of his own action, i.e. not playing at all, unless some misinformed or charitable investor were to give him a full stake. For players with positive ROI, we see that there is always some positive percentage of his own action that produces a better profit than not playing at all.

The optimal percentage of his own action that the player should take seems to increase in a convex way; as the player increases his skill edge over the field, the optimal percentage that he should keep increases faster at higher values of ROI. For both the typical player and the wealthy player, there is a "ceiling" level of minimum ROI at which the player should take all of his own action. We see that this is about 136% for the typical player and about 66% for the wealthy player.

Conclusions and comments

These charts should provide a useful guideline for real-world staking and backing decisions for large-field tournaments. Some practical notes:
  • This analysis was done for the 2010 WSOP Main Event. Most other tournaments (likely including the 2011 WSOP Main Event) have much smaller fields, and, accordingly, have less skewed payoffs and have less extreme utility annihilation effects. So, for a $10k tournament with a smaller field, the optimal percentage of one's own action to keep will increase, and the guidelines in this post can be used as a lower bound to this.
  • Similarly, for tournaments with buyins less than $10k, the optimal percentage of one's own action to keep will increase, and for tournaments with buyins greater than $10k, the optimal percentage of one's own action to keep will decrease. In these cases, the guidelines in this post can be used as an upper/lower bound.
  • Note that every aspect of this analysis holds just as true for the person making the investment as it does for the person doing the playing. If a player and his investor(s) all have roughly the same wealth and utility, then each of them will be doing best by taking on the recommended optimal percentage of the player's action as recommended by this model. For example, when the player has a 100% ROI and wants to maximize the total utility among himself and his investor, the parties will both roughly optimize their expected utility by the player keeping 50% of his action while one investor takes the other 50%. In the case of the 50% ROI player, he would take about 12% of his own action while selling off equal pieces of about 12% each to 7 different outside investors.
  • In reality, players can't know their exact ROI in any given tournament. The best that players can do is form something resembling a maximum likelihood estimate based on their assessment of their own ability, the expected field strength of the tournament, and the tournament struture. This could be modeled as a random variable with some uncertainty (likely Gaussian) about the point estimate. That is, if your best guess of your ROI is 50%, a more accurate implementation would involve your ROI being an unknown random variable with mean 50% and some nonzero standard deviation. Close inspection of the first chart shows that the distance between the ROI curves seems to get smaller as ROI gets higher, which means that, in the face of an uncertain ROI, it's best to "round down" a little for the purposes of plugging a fixed ROI into this model. For example, if you estimate your ROI is about 50% but have a lot of uncertainty about this estimate, you will probably get a slightly more accurate result by using something like 45% in these guidelines.
Let me know if you'd like to see me add the considerations of other types of staking and backing contracts to this model. And, as was the case with my risky site bankroll management model, this is a model with many specific variables (wealth, income, risk preferences, ROI, tournament field size, tournament payout distribution) that would ideally all be tailored to each specific player and each specific tournament on a case-by-case basis.

This is a model that should be of tremendous practical value to all tournament players, so if there is enough interest, I might clean up my spreadsheet and make it publicly available in the future.
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