There weren’t many spots where Russell was truly short or had to deal with short stack shoves, but I did want to break down the math of his A9 call, because at first glance a lot of people think it’s not a good call, but I immediately thought it was probably OK.
The stacks were like this:
Greg Merson: 88,425,000
Jesse Sylvia: 62,825,000
Russell Thomas: 15,900,000
Jake Balsiger: 30,825,000
Blinds are 300,000/600,000 with a 75,000 ante.
It folds to Russell in the SB who opens to 1,500,000, Jake moves all-in from the BB, and Russell calls.
When I was watching the hand in real time, I was pretty convinced that Russell would fold. He took a minute or two to call, and in my work with Russell, he was pretty quick and accurate with his ranges. Granted, things change at the final table, but I feel like the length of time he spent on the call indicated it was the bottom of his range.
In the press conference afterwards, Russell said that he figured Jake would shove any ace, any broadway, and likely some suited connectors. I think against a decent online player, this is probably a fairly good range. Things do change at the final table of the Main Event, so it’s possible that Jake was tighter. I do think we can also discount monsters from Jake’s range. I doubt he would just simply shove with a hand like AA or KK. Let’s do some analysis now:
This is the “base” situation, which I’ve set up to be even tighter than Russell thought Jake was shoving. I’ve had to move stacks around and adjust the blinds to deal with the raise/3-bet shove/call decision scenario, and I’ve adjusted edge to -0.15 here, mainly because ICM overvalues short stacks. If you want more details on using SNGWiz, how to use it for complicated situations like this, and how to adjust for edge, then you can check my videos at PokerVT.com.
As you can see from the picture, A9o is a call, and it suggests he can call even a few hands wider than that, including A8o+, A7s+, KQs, and 33+.
Doing some further analysis, regardless of if AA/KK is in Jake’s shoving range, then Jake needs to be folding the worst aces (A2o-A4o), 22, KJo, QJs, and K9s for this to be a fold from Russell. If Russell was correct about Jake’s range being wider, then his call is actually a lot better and he could call most aces and broadway stuff like KJo+ profitably.
There is perhaps one situation where this could be considered a fold. Due to the fact that there’s real life changing money here at the final table, one can reasonably choose to be tight and wait for a really good situation. We decrease variance by doing this, but we also decrease our earnings. So, I think if Russell was taking this into consideration then he could have folded everything but something like AT+ and 77+, and that’s with Jake modeled as being tighter than Russell thought he was. Still, A9 is very close to this range, so even taking the utility value of money into account, this would be a very small error at best. Making this decision is simply personal preference — one is more profitable and the other is ostensibly less variance.
All in all, I’m proud of the way Russell played. Congrats to him for winning $2.9 million!]]>
First off, let’s look at the payouts for the final table of One Drop and compare them to the final table of the 2011 Main Event, which has a more traditional payout structure:
The One Drop payouts are very high for the 1st and 2nd places compared to the Main Event. Logically it’s easy to see that at the final table of the Main Event it can really benefit a player to be tight and try to move up in certain spots. For example, going from 9th to 5th in the Main Event will get you an extra 5.26% = $1.49 million = 149 $10,000 buy-ins. Going from 9th to 5th in One Drop will get you an extra 1.7% = $725 thousand = 3/4 of one $1,000,000 buy-in. However, the broadcast gaffe was specifically about what short stacks should do with 5 players left if there was a massive chip leader, so let’s analyze this situation with the two different payout structures.
Normalizing, let’s say the blind level is 250k/500k with a 50k ante, 5 players left with 100m total chips in play, one player having 80m chips and the other 4 having 5m chips each. If we are in the small blind with a 5m stack and shoving into the big stack who will call with 25% of hands and are disregarding edge, our profitable shoving range is 37% of hands with the One Drop payout structure and 21% of hands with the Main Event payout structure. Likewise, if we are in the small blind with 5m and shoving into another 5m stack who will call with 25% of hands, our profitable shoving range is 100% for the One Drop and 81% for the Main Event.
Simply put, because of the top heavy structure of One Drop, chips have more value and survival is marginalized because the value gained from moving up a payout is not as much as it would be from potentially accumulating chips and trying to get up to 1st or 2nd place.
One thing that was interesting to me, however, is the huge “bubble” at 3 players left in the One Drop. The jump from 4th to 3rd is about $1.7 million, but the jump from 3rd to 2nd is almost $6 million. From the chart above, you can see this is a 13.5% jump, while the 2011 Main Event only has an increase of 4.99% from 3rd to 2nd. So, it follows that this “bubble” is very important and it would be correct for players to tighten up. Here are some numbers:
4 players left (85m vs 5m vs 5m vs 5m):
One Drop shoving range BVB small into big (25% call): shove 24%
Main Event shoving range BVB small into big (25% call): shove 18%
3 players left (90m vs 5m vs 5m):
One Drop shoving range BVB small into big (25% call): shove 14%
Main Event shoving range BVB small into big (25% call): shove 19%
As expected, with 3 players left it is beneficial to tighten up to try to get into 2nd place.
In summary, basically the only time players should be tighter at the One Drop final table compared to a more normal payout structure is when it is 3-handed. In other situations, players in One Drop should be looser. Note that even though all this analysis was done with push/fold examples the high value of chips applies even for normal, non-push/fold play.
One other thing to mention here, as well, is that all this analysis is really done in a vacuum. When we take real life into consideration then I feel that the gap between how people should play at the One Drop final table versus the Main Event final table should be even wider. The players who are in One Drop are all either high level players with lots of money or rich businessmen, but at the Main Event that’s not necessarily the case. Getting an extra $500k is very likely not a major deal for someone in One Drop — it’s only half of the buy-in and probably a fraction of their net worth. For a Main Event final table participant, getting an extra $500k can be a very big deal, though. I am reminded of the hand where Paul Wasicka folded a straight flush draw 3-handed at the Main Event final table when he almost certainly would have gone with it in a $10 online tournament. The positive effect of large sums of money in real life can, for better or worse, cause you to ignore the mathematically correct play.]]>
Here’s a restatement of the problem:
You deposit $400 in an online casino and are given a $100 bonus immediately, so you have $500 to bet with. You can withdraw only after betting a total of $2500. Let’s say you play a game where you flip coins and if you win you get 1.99 times your bet and nothing if you lose. This has a 99.5% return like blackjack, but I’m abstracting it because in blackjack you can run into bad EV spots where you make a bet and then don’t have enough to split or double down. The table limits for this game are minimum bet $1 and maximum bet $100. How much should we bet to maximize EV, and why?
The solution is to bet as much as you possibly can, repeatedly, except for a couple of special cases. The most obvious case is where you need a bet smaller than the maximum in order to finish the wagering requirement (e.g. if you have wagered $2450 then you only need to bet $50 instead of $100). The other case is when the current account balance is greater than or equal to the remaining wagering requirement (e.g. if you have wagered $2000, have $500 left on the wager requirement, but have a balance of $550). In the latter case it doesn’t matter what bet sizes you use, as you’ll see below.
Think first about the problem and the hints I gave. If the problem had a trivial answer it wouldn’t be very interesting at all. As I stated, most people intuitively come up with a handful of answers and those are all wrong. The hint I gave about martingaling’s EV in comparison to flat minimum betting should lead you to one of two conclusions. If you don’t think martingaling has a higher EV then you are of the persuasion that the bet size doesn’t matter, and I already stated that isn’t right. So, martingaling at some point has a higher EV. Now you just need to figure out when and why. In actuality, the increasing bet sizes of martingaling is a consequence of knowing that large bets give higher EV in this situation, rather than a good place to start thinking about the problem. A deceptive hint on my part.
As a general method of reasoning, when you have a lot of possible options and it wouldn’t help or would take too long to think about individual cases, it helps to look at things like extreme cases, boundary conditions, and infinite iterations. This is very useful in fields like math and engineering. If you watch my videos on Poker VT or read some of my other blog posts, you’ll see me saying things like “Even if this guy called with 100% of his hands, it would be a good push” or “Even if you have AA you should fold.”
So, consider 1000 of these bonuses. You will earn $100k in bonus money. If you bet $1 per hand then you will almost never bust, and you will have wagered 1000 * $2500 = $2.5 million. With the -0.005 return on every dollar bet, you’ll have lost $12.5k for a total profit of $87.5k. If you bet $100 per hand then you will bust quite a lot. If you lose all your money then you don’t have anything else to do, you have only completed part of the wagering requirement and move on to the next bonus with a fresh $400 deposit and new bonus. At the end of 1000 of these bonuses you may have only wagered $1.5 million. The -0.005 EV here means you will have lost $7.5k for a total profit of $92.5k.
Congrats to user Ka.Yung from Poker VT who figured this out early and his reasoning why is absolutely correct as well. I tried to throw him off with a false response but got no reply, so I don’t know if he bought it or not.
My refutation was this: “say you are successful with the $100 bets, then isn’t your EV is the same as with the $1 bets — you expect to make $87.50 with the bonus because you’ve wagered $2500 in both cases” which is deceptively wrong. At the very least, if you understand that we don’t care about variance then you think that $1 bets and $100 bets have the same EV. You know you are going to bust a lot with the $100 bets, though, so you should correctly reason that the times that you don’t bust with the higher bets, that your wins will be high enough to compensate for those times you do bust. For instance, if you never bust with $1 for an expected result of $487.50, and you bust half of the time with the $100 bets, then you would expect the ending balance when you don’t bust with $100 bets to be $975. It would be extremely rare to be up 575 $1 units over your initial deposit like this, but if you don’t bust when betting $100 then to be up 5.75 units is not surprising at all. Anyway, as I showed in the solution above, you would actually expect to have a slightly higher profit than this $975 anyway.
One person tried looking at the extreme case of depositing $2500 and then betting the $2500 all at once. This fundamentally changes the nature of the problem and indeed the answer is trivial in this case. You will complete the wagering requirement every time regardless of how much you bet, so any bet size gives the same EV here. We can only increase our EV when we have an opportunity to go broke before meeting the wagering requirement.
This problem is pretty easy to write a Monte Carlo simulation for, and I did, similar to what I did for my megapost on variance in SNGs. In practice the result for the $1 bets is indeed around $87.50 per bonus but for the maximum bet the result is above $90 over a statistically significant number of bonuses.]]>
In my history of asking this problem, there are some common solutions that people come up with:
Bet $1 because you have less risk of busting.
When I did bonuses like this years ago, betting $1 is exactly the method I used precisely for the reason that I was risk-averse and had a limited bankroll. Such reasoning doesn’t apply to this situation, though, where we don’t care about variance, only EV.
Bet whatever you want because it doesn’t matter.
Some people reason that it doesn’t matter what bet we place, since a $100 bet has the same expectation as a series of 100 $1 bets. This is true, however despite the truth of this statement it’s not a valid reason as a solution to the problem.
Some people come up with the martingaling strategy, doubling bets every time you lose and such. Many people believe this is not only the best strategy, but one in which you can’t lose. This, also, is not right. Plenty of good information on the subject is available here.
A note about martingaling, and here’s a hint about the correct solution: martingaling has no worse EV than betting $1 repeatedly. Would it be possible for me to convince you that martingaling has a higher EV than betting $1 at a time?
The solution will be posted in a couple of days.]]>
I originally thought of this in the old days when online casino bonuses were prevalent. If you’re not familiar with them, you would deposit some amount, get a bonus, and have to meet a wagering requirement to withdraw the bonus. So you’d do something like deposit $100, they’d give you $20 on top of that for free, and you’d need to wager $1000 at blackjack to withdraw. Blackjack pays back around 99.5% if you play perfectly, so you’d end up losing $5, thus profiting $15 after the bonus. This is actually how I started my bankroll many years ago.
So, here’s the problem:
You deposit $400 in an online casino and are given a $100 bonus immediately, so you have $500 to bet with. You can withdraw only after betting a total of $2500. Let’s say you play a game where you flip coins and if you win you get 1.99 times your bet and nothing if you lose. This has a 99.5% return like blackjack, but I’m abstracting it because in blackjack you can run into bad EV spots where you make a bet and then don’t have enough to split or double down. The table limits for this game are minimum bet $1 and maximum bet $100. How much should we bet to maximize EV, and why? Does it even matter? If so, why? If not, why not?
Time and variance are not factors in this problem. We don’t care that making lots of small bets takes more time than making a few large bets and we only care about maximizing EV.
Post answers or discussion in the comments. I’ll reveal the answer in less than a week and may drop some hints if nobody gets it.]]>
After I had finished making that video on variance I decided to write my own Monte Carlo simulator. Since it was just a little project for my own usage I didn’t do anything fancy, make it nice for user input, or create any executables. It’s sloppy, obfuscated, and has no real documentation either. You can grab it here if you like. If you can find RVG’s old ROI simulator program you can use that for games with fewer than 7 payout spots and the results will agree with what my program produces.
For those of you who have seen my variance megapost on Poker VT, a lot of this post is straight from it.
Before giving you the output for various runs of this program I’ll explain what the output means and how to interpret it:
$6.00+0.50 9-man game
Payout distribution: 50.0% 30.0% 20.0%
Finish distribution: 12.7% 12.5% 12.6%
Theoretical ROI = 4.84%
100000 simulations of 1000 games:
The top part of this describes the type of simulation we’re running. A standard $6.50 SNG with normal payouts and finishing in 1st place 12.7% of the time, 2nd place 12.5% of the time, and 3rd place 12.6% of the time. This corresponds to a 4.84% ROI. It also prints out the ROI and how many buy-ins it represents. It then tells the number of games in the sample size and how many simulations were run.
Below that we have confidence intervals (CI) for ROI, the biggest downswing and the lowest drop from the starting point. So, if you know you are a 4.84% ROI player, in a 1000 game sample 90% of the time your actual ROI will be between -2.88% and +12.65%. Remember that confidence intervals are centered over the median, so 5% of the time the actual ROI will be worse than -2.88% and 5% of the time it will be better than +12.65%. If someone said to me “I believe I am a 4.84% ROI player but my results over my last 1000 games are -3% ROI” then I could confidently say to them that there is less than 5% chance that they are actually as good as being a 4.84% ROI player.
Likewise, 90% of the time we can expect to have a downswing in that 1000 game sample between 21.82 and 72.35 buy-ins. 5% of the time it will be worse, 5% of the time it will be better. If you knew you were a 4.84% ROI player and you had a downswing of 80 buyins over a 1000 game sample, for example, you could say you are running exceptionally bad.
The lowest drop represents the largest drop below the starting bankroll. This is, by definition, going to be equal to or less than the downswing value. This is a good way to know a risk of ruin for a particular bankroll size. Remember that because the CI is centered on the median, that means that the risk of ruin over 1000 games with a bankroll of 54.8 buy-ins is 5% (100% – the “middle” 90% – the “top” 5%). You can look at the downswing values this way as well. You could say that 95% of the time we are going to have a downswing of more than 21.82 buyins.
Now, compare this data and interpretation for the 50% CI:
50% CI for ROI: 1.69% to 8.00%
50% CI for downswing: 29.55 buy-ins to 48.89 buy-ins
50% CI for lowest drop: 5.38 buy-ins to 26.82 buy-ins
What does this mean? It means that as our confidence goes down we are able to get closer to the median. For ROI this will start to converge on the true ROI. For downswing and lowest drop it will start to center on the most “common” downswing. From these numbers we can make interpretations such as “75% of the time our downswing will be 29.55 buy-ins or more” and “75% of the time our downswing will be 48.89 buy-ins or less”.
When we finally get down to 0% CI, we’re at the median:
0% CI for ROI: 4.84% to 4.84%
0% CI for downswing: 37.58 buy-ins to 37.58 buy-ins
0% CI for lowest drop: 13.60 buy-ins to 13.60 buy-ins
As expected the ROI converged to the theoretical ROI, and it always will for a large enough number of simulations. We also see the exact medians of downswing and lowest drop. The 13.60 buy-ins would correspond to a risk of ruin of 50% (half the time the drop will be better than the median, half the time the drop will be worse than the median).
OK, so that information gives you an idea of how to consider variance as you’re playing, but what if you’re sitting there saying “I’m a new player and I have played X games and my ROI is Y, how can I use this data?” This is a much more difficult question to answer. Per the comment about ROI claims above if you had an ROI of -10% after 1000 games at the $6.50 level, you could pretty confidently say that your actual ROI is not 4.84% or close to it. If you look at the difference between the high and low ROI, that may help though. For example consider the two outputs:
Theoretical ROI = 4.84%
99% CI for ROI: -7.29% to 16.97%
Theoretical ROI = -0.31%
99% CI for ROI: -12.02% to 11.74%
You’ll notice the difference between the high and low ROI in the first case is 24.26 and in the second case is 23.76. So, with 99% confidence we’re about +/- 12% ROI over 1000 games with these parameters from the theoretical. You could make an educated guess that over 1000 games if your ROI is -10% that your true ROI is probably in the -22% to +2% range with a high degree of accuracy.
With all this data be sure to keep in mind that you don’t play a certain way constantly. You should continuously be getting better. As your game changes, if you want to estimate your ROI try to give more weight to recent games.
Below are some more interesting runs of the program.
These first two show off the difference in variance between a 1000 game sample and a 5000 game sample with all other parameters equal:
$15.00+1.00 9-man game
Payout distribution: 50.0% 30.0% 20.0%
Finish distribution: 12.5% 12.5% 12.5%
Theoretical ROI = 5.47%
100000 simulations of 1000 games:
$15.00+1.00 9-man game
Payout distribution: 50.0% 30.0% 20.0%
Finish distribution: 12.5% 12.5% 12.5%
Theoretical ROI = 5.47%
100000 simulations of 5000 games:
Here are some numbers for 180 man games:
$11.00+1.00 180-man game
Payout distribution: 30.0% 20.0% 11.9% 8.0% 6.5% 5.0% 3.5% 2.6% 1.7% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2%
Finish distribution: 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8%
Theoretical ROI = 32.00%
100000 simulations of 1000 games:
Lastly, someone asked me about MTTs. Because field sizes change a lot it’s hard to do this sort of analysis. But if your average field size was about 1000 people, these would be the sorts of numbers you could expect if you knew you were a decent winner:
$55.00+5.00 1090-man game
Theoretical ROI = 53.29%
250000 simulations of 1000 games:
I went in and tried to play a scheduled MTT they had, but it only got 3 entrants so we all just left. I honestly got goosebumps going into the place, though. Ended up going across the street to the Golden Nugget and won a MTT there though! Maybe I’ll throw some info up here about all of that, but in the meantime I wanted to get some news out.
Poker VT has a good 10 or so videos from me. Some review of my own play, some live play at micro, low, and midstakes SNGs, a few purely theory videos, and hand history reviews for the winners of the SNG contest. These should be coming out over the coming weeks and months. The SNG contest is no longer active.
My coaching rates have dropped and I’m being a bit more public about it. In the past I always tried to keep a very small number of students (usually 2 at most) though now that I have to schedule around what games are available and running live I’m willing to take on more students and at a cheaper rate. If you don’t know, I won the FTP SNG high stakes leaderboard before and lately have some pretty decent turbo/super-turbo MTT results as well. For coaching info check this page.
I still intend on continuing with the strategy blog, so check back for that stuff]]>
Here’s a recap of some of my prior strategy posts:
And a few other useful posts:
My future strategy posts will be probably a lot more specific and with a lot more detail than the ones I’ve written before.
Lastly, I’m updating my twitter account more, so be sure to follow me there for updates.]]>
No really they’re worth reading, so be sure to go check them out.
I’ve got some interesting stuff in the pipeline, one is a review of probably the first poker book I’ve read in almost 2 years, and it’s not me making fun of T. J. Cloutier. The other thing is I’m working on an interesting theoretical study related to some MTT data analysis. If you have a lot of MTT hand histories I’d like to hear from you so I can use them in my study. All results will be anonymous, although if you participate I’ll tell you who you are. If you have 500 or more MTT hand histories let me know. The only poker games I can use in my study are NLHE MTTs, and so that means no SNGs, MTSNGs or cash games.
Also the first SNG contest in months over at PokerVT has wrapped up. For better or worse there were 27 participants, and it paid out $400 to 6 people, with $145 to the top prize winner, MxKlptz. To compete all you need to do is play in 8 $3.40 SNGs with me over a month’s time on PokerStars at 5 PM Eastern on any Sunday. Considering that only people who have played 8 games have won prizes and that 15 of the 27 people in December played 8 games, that’s almost 100% ROI in EV on top of whatever you make while playing the games! For more info about the contest check here. And if you’re signing up to PokerVT don’t forget to use discount code NotAProblem to get a better-than-usual discount.]]>
Because prices and deals change I’m not going to talk about specific models or parts too much but rather about manufacturers, stores, and specs you should be aware of.
The hardest part of buying a laptop is finding one with an appropriate screen size and resolution with decent performance for the right price. Keep in mind that a laptop is generally going to be more expensive than a desktop with similar specs. The first thing you need to ask yourself is if you’re going to be moving the laptop around a lot. Smaller laptops are better for portability, but larger “desktop replacement” laptops that have screens of 17″ and up are the best for poker. One option, as well, is to hook a larger monitor up to a laptop to use both screens, or just the monitor screen for tables and the laptop screen for lobbies and other things.
A lot has been said about laptop manufacturer reliability, and you can certainly find a lot of studies. I’d suggest checking out some like this and making your own decision. If someone tries to give you advice like “Don’t buy X brand because I had one and it broke after a month!” you should just immediately disregard everything they say. Yes, everything has a certain failure rate, and some makers are better than others, but the differences are usually small enough to be meaningless. In the linked article above you see a 2 year failure rate of 10% for the best maker vs 16% for the worst. If you’re saving several hundred dollars or getting exactly what specs you want in a laptop by taking a slightly higher failure rate then it’s probably worth it.
Screen size and resolution: This should be the first thing you look for. If you want to play up to 12 tables with no overlap then you’re going to need a resolution of 1920×1080 pixels (also called 1080i, 1080p, or Full HD), although slightly larger 1920×1200 (WUXGA) resolutions are also available (this will give you some space for your taskbar and such). Most of the time you will need to get a 16″ or larger screen in order to find resolutions this high, althoughI feel like such high resolutions on a 16″ screen make everything too tiny and I would go for 17″ at a minimum.
Screen coating: Glossy displays are more expensive, brighter, better looking, and easier on the eyes for most situations. This can be a matter of personal preference though, but personally I wouldn’t even consider getting a laptop without a glossy screen.
Processor: Honestly if all you’re doing is playing poker, browsing the web, running stuff like PT3 or HEM and other related poker software, this doesn’t matter a great deal although I would strongly suggest going with at least an Intel Core i5. i5 is the sweet spot right now in terms of price and performance and has been for all of 2010. If you end up getting a great deal on the slightly worse i3, that’s OK, but stay away from AMD Athlon. Not only does it have considerably worse performance, but if you find an Athlon as a component in another laptop then it’s a budget laptop, will have worse overall specs, and can run into some overheating issues because of sub-par builds.
Operating system: Windows 7 Home Premium should be the standard option for most laptops. If you have an option to get Professional or Ultimate, don’t. The only relatively useful feature is in Ultimate which has BitLocker drive encryption, however you can do the same thing for free with TrueCrypt, which I mention in my security post. If, for whatever reason, you decide you do need one of the more expensive versions you can activate it online and unlock the features without doing anything complicated like reinstalling.
RAM (memory): Because laptops ship with Windows 7 now, the base recommended RAM is 4 GB. I would suggest going with 6 or 8 GB however. Increasing the RAM allows more stuff to stay in memory which means it won’t have to be swapped out to the disk as much. Not only does this increase speed but it reduces temperature and puts less strain on your hard drive. If you can’t configure your laptop online or if you can and it would be prohibitively expensive then you can always buy some RAM cheaply and install it yourself. Laptop memory is extremely easy to install as you can see in this video and is one of those things that even someone with no computer experience could do.
Video card: Budget laptops will frequently only use on-board/integrated video, and unless you play a lot of the most recent PC games out there this is fine, however there are two concerns for poker players. First is if you actually find a laptop with integrated video the other specs probably aren’t going to be to your satisfaction, and secondly if you do get one with integrated video then it won’t have useful video outputs, like DVI and/or HDMI, for a second monitor. Trying to drive a decent monitor using USB or VGA and a VGA to DVI adapter is not going to be pretty. Things like RAM or manufacturer don’t matter much, although NVIDIA is slightly better than ATI in general.
Hard drive: You actually have some options here aside from just picking a size that’s useful for you. If you’re willing to pay a bit extra you can get a system with two drives in it that will do RAID 1 (mirroring), so if one drive fails you still have your data. Slightly higher price, very slightly lower performance, but greatly increased reliability. You should be backing up important stuff anyway and unless this laptop will be the computer you use for absolutely everything then I would not suggest upgrading. Very few makers will offer this option, and if they do it will only be on a handful of laptops.
The other option you may have is getting a SSD (solid-state drive). Basically think of it like a big USB flash stick in place of your hard drive. These are quite expensive though. A standard 400GB laptop drive is only $40, but a 256GB SSD costs over $400. The performance increase is very noticable though. The hard drive is usually the bottleneck of a system and SSD drives are MUCH faster than a HDD. This means faster bootups, faster application launches, better application performance, and better overall system performance. SSDs are also less susceptible to damage from drops since there are no moving parts like a HDD. Because of the prohibitively high price, however, I would not suggest getting a SSD. The price is usually only going to be right if you get a very small drive, and then you will likely only use the computer for poker because the small drive will limit it’s usefulness for other stuff, so then you don’t really need the performance.
Warranty and protection plans: Many companies will offer you extended warranties and protection plans. I’m a big fan of things like accidental damage protection. It’s the most expensive option, but basically if anything ever goes wrong whether you did it or not they’ll fix it, replace it, or give you a newer model if they don’t have the parts/model available. If you travel with the laptop a lot you will definitely want this, but even if you don’t travel, just regular usage over years will cause minor problems like case cracking, dead pixels, power connector becoming loose, RAM or a HDD dying, etc. If you are spending around $1k or more on a laptop it is a good idea to spend $200 or so to get a 3 or 4 year plan. I’ve found most people tend to replace laptops in about 2-3 years, in part to get the latest and greatest, but also in part to get something that doesn’t have a few things broken on it.
Other stuff: Speakers are not a major concern on laptops, none of them will sound fine and it’s easy enough to hook them up to a cheap system that sounds decent if you are so inclined. Laptops ship with DVD burners as standard, but some may have Blu-Ray. If you have the option then you shouldn’t pay extra for Blu-Ray if you won’t use it, obviously. Every laptop has wireless included so you don’t need to worry about that. If there’s an upgrade for something like 802.11n don’t bother with it.
Getting the best deal
As far as manufacturers and places to shop go, you should be aware of what companies will allow you to configure your laptop and which won’t. Configured laptops are generally more expensive than pre-made models, but you will be able to get exactly what you want rather than compromising on certain specs. Dell, HP, Sony, Lenovo, and Toshiba all allow you to configure laptops on their website. Asus, Acer, and Gateway do not.
Whether you are going to buy a pre-made laptop or configure one yourself, be aware you can frequently get up to 5% cash back and find all available coupons and sales for stores at FatWallet. Also be sure to check out TechBargains. If you’d like to peruse a good selection of pre-made laptops that will have decent prices, check out Newegg or Amazon. For individual parts (like RAM) check out Pricewatch and for anything related to cables or adapters check out Monoprice.
So, after all this I feel like I should share some of my personal experiences and feelings on the matter. 10 years ago I felt like Dell was definitely the best but lately I’ve liked HP and Sony a lot more. I have put my Vaio through hell and it’s held up remarkably well after 2 years, though the only reason I bought it was because Sony has a nice program where you can get a discount by trading in old laptops, even if they are broken, and at certain times they even give you an extra $100 on top of that. In terms of value for money I feel like HP is the best, though. When you compare HP to Dell, for instance, you will frequently be able to get equal or better specs all around and for $100-$200 less.
The pre-made laptops are really starting to catch up, though, especially if you aren’t picky. As a plus, because you don’t have to get someone to make it you will get it a lot faster than the 2-3 weeks it takes for a place like Dell or HP to deliver.]]>