Part two of two: 10 tips to keep in mind when trading in your fantasy league…

Welcome back to my list of ten tips for making winning fantasy trades. If you didn’t read last week’s part one installment, and its five tips, you might want to pause to do so now before diving into the remaining five below.

Don’t bank on breakouts, because the near future tends to look a lot like the recent past

Every year poolies have high hopes for lots of players to break through; yet when the dust settles, it rarely happens. Take last season for example. A total of 19 non-rookie forwards posted 65+ points, with only one (Evgeny Kuznetsov) not having previously tallied 55+ points in a season. It was a similar story with defensemen, as although last season 26 non-rookie defensemen posted 40+ points, all but one (Rasmus Ristolainen) had previously posted 35 or more in at least one prior season.

What this means is you should look for ways to capitalize on early season hype surrounding young unproven players to at least entertain trading them to a “breakout goggles” GM in order to get, in return, an asset who might be less enticing but figures to outperform the hyped player when all is said and done. By the same token, be careful not to fall into the trap of paying inflated value in trade for a player you think is primed for a huge breakout, since that’s unlikely to actually occur in the near term. Instead, focus on players your fellow GMs might have wrongly pegged for a downturn, or on “steady eddies” who don’t have the same hype as potential breakout players but who’ll likely cost you less to obtain and stand a better chance of outpointing the anointed breakout players for at least this season.

Luck matters, but don’t look at it in a vacuum

Thanks to the availability of advanced stats, poolies are now able to gauge how lucky or unlucky a player might have been in a particular season. Things like IPP (which identifies how often a player tallied a point when a goal was scored while he was on the ice) and team shooting percentage (representing the shooting percentage of a player’s team while he was on the ice) go a long way in indicating whether (and, if so, to what extent) a player might have benefitted from good luck or been hurt by bad luck.

But when assessing luck for purposes of shaping a trade, make sure not to look at numbers in a vacuum. First, determine where a player’s luck data puts him vis-à-vis comparable players, meaning that a top six forward who skates 20+ minutes per game and gets ample PP Time should have his luck judged versus other premier players, not fourth liners; and similarly, minute eating rearguards shouldn’t be compared side-by-side with third pairing defensemen. Next, compare a player’s luck metrics to those from his prior seasons, preferably while with the same team. The more consistent his numbers have been, the more likely it is that if something sticks out and there isn’t a good reason for it (e.g., vastly changed role), then luck was at play. Once you’ve done these things, you can find out which players likely benefitted from unsustainable good luck (and thus are at risk of seeing their numbers go down going forward) or were hampered by unsustainable bad luck (in which case they should rebound).

To see how this type of analysis can reveal unsustainable good luck, let’s examine Marc-Edouard Vlasic. Last season he not only posted a career high 39 points at age 29, but did so in only 67 games after never previously having scored above 36. And sure enough, his luck data screams regression. Among 124 d-men who skated 1000+ minutes at 5×5 last season, his 42.9% IPP at 5×5 put him 17th, and his average in his prior five seasons was only 28.4%. Moreover, Vlasic’s 10.22% team shooting percentage while he was on the ice at 5×5 put him 2nd among the same 124 d-men and not only well eclipsed his previous career best of 8.50% but also marked only the fourth time he’d bested 7.56%.

Whereas luck data suggests Vlasic is a player to trade away or to at least avoid trading for, the opposite appears to be true for Mike Hoffman, as similar data seemingly validates his 2015-16 breakout. After all, Hoffman followed up a 2014-15 season that saw him post a 76.5% IPP (54th among 262 forwards with 750+ minutes at 5×5) and 10.54% team shooting percentage (13th) with an 81.5% IPP (8th among 125 forwards with 1000+ minutes at 5×5) and 9.56% team shooting percentage (13th) for 2015-16. Thus, just as Vlasic’s breakout is suspect, Hoffman’s seems for real.

As 2016-17 unfolds, look at luck metrics to see if players who appear to be overperforming indeed are benefitting from unsustainable good luck or instead are putting up their solid numbers based on a sustainable situation. By the same token, players performing worse than expected should be judged to determine whether they’re being victimized by unsustainable bad luck (possibly making them a good trade target) or, instead, might just be seeing things take a turn for the worse (in which case you might want to trade them while they still have any remaining appeal to other owners).

Even in points only leagues, pay close attention to special teams metrics (plus OZ%)

It’s very difficult for a skater to be elite unless he receives significant power play time. Are you holding out hope that one of your forwards has a chance to be an 80+ point guy? Forget it unless he’s clocking 3:00+ per game on the PP, as among the 34 instances of forwards tallying 80+ since 2010-11 none averaged less than 3:08 per game on the PP in their 80+ point season(s). Or maybe you think one of your forwards can raise his production to the 50-60 point range despite not being a solid PP scorer? There too the data suggests it’s a longshot, since among forwards who had less than ten PPPts last season, only one (Brad Marchand) managed 60+ total points and just six (Jonathan Toews, Justin Williams, Leon Draissaitl, Milan Lucic, Brandon Saad, Jeff Skinner) had more than 48 total points. Long story short – PP Ice Time and PP production are hugely important to overall scoring.

You also have to look at players saddled with shorthanded duty, as even a couple of minutes per game takes a toll on overall scoring. For example, as pointed out in my article for the Fantasy Guide, over the last two seasons, there were 97 instances (47 in 2014-15, 50 in 2015-16) of forwards averaging 2:00+ per game in SH Ice Time, yet only two (Patrice Bergeron and Ryan O’Reilly in 2015-16) who scored 60+ points or at a 60+ point pace in that season. And chances are these two did so only because they averaged merely 2:05 (O’Reilly) and 2:11 (Bergeron) per game of SH time, which barely put them over the threshold, plus compensated by having the highest (O’Reilly) and 16th highest (Bergeron) average total Ice Time per game as well as the 22nd highest (O’Reilly) and 43rd highest (Bergeron) average PP Ice Time per game among all NHL forwards.

Also, although many poolies continue to dismiss advanced stats/metrics, one that’s both easy to grasp and incredibly telling (even for points only leagues) is OZ%. It measures the percentage of time, at 5×5, a player starts his shift via a faceoff in the offensive zone. In other words, if a player has a 60% OZ% that means 60% of the time he’s out there for faceoffs in the offensive zone, while the other 40% isn’t.

What you find with OZ%, is when it’s low it stands in the way of all but the very best forwards being able to produce; and even those players will likely have points ceilings because of it. The “magic OZ% line” for forwards seems to be 45%, as in the past three seasons there’ve been 199 instances of forwards who played 60+ games yet were saddled with less than a 45% OZ% at 5×5, and just ONE instance (Phil Kessel in 2013-14) of such a player posting 60+ points in that same season.

So before you make any trades in any leagues (even points only), be sure to check out OZ% data as well as PP and SH Ice Time and productions of the forwards involved, since it’s very telling information.

You need to vary negotiation techniques depending on who initiated the trade talk

Every GM’s radar goes up when approached for a trade, with many becoming immediately suspicious that the person reaching out to them is trying to pull a fast one. Knowing this, when you’re the one doing the approaching, you might try and misdirect or be vague by not starting with what you really want. Or you can first ask for a player you think they’d never deal, as that way if/when they say no you can shift to the player you wanted all along. Of course this might not be the optimal technique every time; but it’s something to consider when “making the first move” on a possible trade.

On the other hand, if you’re the one being approached, you’re in control and can often use that to your advantage. You want to start by asking for a deal that clearly favors you, so if the other side takes it, great; if not, then they counter, which not only will tell you a lot about whether there’s a good chance of a deal being made that you’ll be happy with but also puts control squarely back with you. The key is if you make it so the other GM can mentally feel like they pushed you away from what they thought was what you really wanted, that gives them mental comfort to make a deal which, in truth, still favors you.

You can’t make the “right” trade unless you understand the impact it should have in the standings

If you follow all the other advice in this column (plus last week’s part one), you still might not “win” a trade unless you’re fully aware of the likely impact the deal will have in the standings. Some categories (e.g., SOG, SV%, Saves, GAA, Hits, Blocked Shots, FOW, and Assists) are “higher output,” meaning that most players will contribute a lot of output that is factored into the standings. This is in contrast to “lower output” categories like +/-, Goals, SHP and PPP, where it’s rare for any player to contribute much output in any of these categories.

Something to keep in mind is it’s more difficult to affect standings in higher output categories than lower output categories. This is because by and large the higher outputs of most players tend largely cancel each other out, making it harderdifficult to help (or hurt) your team in the standings for a higher output category. As a result, you should be very careful about trying to trade for gains in a higher output category, especially if, in doing so, you’ll hurt your team in a more volatile lower output category.

This situation becomes even more pronounced as the season progresses. Thus, once you get past the 60 or even 50 game mark, you should be more willing to “punt” a higher output category, since if you’re way behind it’ll be all the more difficult to catch up. And the good news is many GMs are unaware of the difficulty in making inroads in a higher output category, which means if you have guys who are strong in one or more higher output categories but far less so in lower output categories, you should generally be willing to trade them since you probably can get good value from a team who’s still in the thick of things in higher output categories while securing, in return, a player who can actually make a difference for you in the lower output categories where you’re still competitive. By the same token, if you’re way ahead in a higher output category later in the season, then others will likely have a harder time chipping away at your advantage, which leads to the same end result – you being able to trade away value in that higher output category. In sum, you should be willing to trade away players who one-dimensionally excel in higher output categories you might be doing well (or poorly) in, although be careful that you don’t squander value in other lower output categories at the same time.

There you have it – my 10 tips for winning fantasy hockey trades. Of course I can’t guarantee these methods are foolproof or, as noted last week, applicable to all leagues; however, if you follow them when possible you should end up bettering your team more often than not. Good luck, unless of course you’re in my leagues……In that case, forget everything you just read!