Kodo Hockey← St. Louis Blues
2026-27 season preview
St. Louis Blues
42-33-993 pts18th of 32
Goals for
2.98
24th in the league
Goals against
3.03
19th in the league
Power play
17.6%
26th in the league
Kodo projects the St. Louis Blues for 42-33-9 (93 pts). In a banger league, the fantasy value runs through Dylan Holloway and Jake Neighbours. 4 core skaters project to rise and 3 to slip. Joel Hofer is the projected starter.
Your categories · using the preset above
Regression watch
finishing/on-ice luck ran hot — expect some pullback off last year's line
The crease
Joel Hofer
Joel Hofer projects the crease (~48 starts), but Jordan Binnington (~34) makes it more timeshare than lock
Contents · 12 sections
ReportedMason McTavish — It's just 2 games but top line has no pts together. Snuggerud had 1G-1A in DAL but that was ENG and an assist on McTavish goal.
Monty: "They're putting forth good effort. It's not coming together right now for them, like the production we saw the last 20 games. It'll get there." · @jprutherford ↗2026-10-04
Injury noteAlexey Toropchenko — now Out · CBS2026-10-03
ReportedJustin Barron — The St. Louis Blues flew in this morning after beating the Stars last night, so no morning skate for the team.
However, there’s two Blues on the ice. Justin Barron, who was claimed off waivers less than a week ago, and Ross Johnston. https://t.co/pkRGzoKkFM · @OHaraSports ↗2026-10-03
ReportedAlexey Toropchenko — Alexey Toropchenko has an upper-body injury and is “more than day-to-day” according to Jim Montgomery.
More is expected to be known on Saturday. #stlblues · @StLouisBlues ↗2026-10-03
ReportedJoel Hofer — Joel Hofer and the St. Louis Blues blank the Dallas Stars 4-0 on opening night at the AAC.
It's the Blues' first win in Dallas since December 14, 2021, snapping a four-game winless skid (0-4-3).
@DLLS_Stars Post Game Show is coming up soon! · @OwenNewkirk ↗2026-10-03
Each item names its source. Kodo's own projected line changes are not reported here.
25-2626-27Change
Goals for2.824th2.9822nd+0.18▲2
Goals against3.120th3.0318th-0.07▲2
Power play17.626th19.8426th=+2.24~
Penalty kill76.725th77.8823rd+1.18▲2
Faceoffs49.419th48.3330th-1.07▼11
Points percentage0.52424th0.55418th+0.030▲6
How these projections are made
Both bars share one scale per row — the wider of the two league ranges — so a bar that moves is a number that moved. The grey tick is that column's league average, and the arrow is the change in league rank. Goals-against and expected-goals-against rank ascending, so low is good and green always means improved.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
They finished stronger than they started — +37 points of win percentage between the first quarter and the last.
Oct–Nov10-09 – 11-18
30%6-14
for2.75
against3.90
Nov–Jan11-20 – 12-31
43%9-12
for2.19
against3.10
Jan–Mar01-02 – 03-04
40%8-12
for2.90
against3.35
Mar–Apr03-06 – 04-16
67%14-7
for3.43
against2.29
Light nights
20.2%30th
17 of 84 games
Four-game weeks
629th
3 weeks of two or fewer
Back-to-backs
1219th
roughly one backup start each
Playoff-week games
116th
over 3 weeks · 3 back-to-back
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
14Nov
13Dec
15Jan
13Feb
10Mar
13Apr*
6* part of a month — the season opens and closes mid-month.
Lineup notes
This is the lineup the club can ice on opening night, not its best-case group. A player expected to miss more than the opening month is left out and his slot goes to whoever takes it — he keeps his projection below, he is out of the lineup, not out of the season.
Projected ice time totals 298.1 of the 300 skater-minutes a game has. Power play 3.16′ on the first unit, 1.79′ on the second; the kill 2.35′, 1.67′ and 1′ across three units — all measured.Change this lineup →
Forward lines
L1
L2
L3
L4
Defence pairs
D1
D2
D3
Special teams
Scratches & depth
0.64 points a game and 15.7 minutes walked out of the lineup — about 6 points over a season.
Stepped up without him
playerwithw/outswing
Points per game with him in the lineup against the games he missed. Not a controlled experiment — absences cluster around injuries, so some of these games were missing other players too. Every absence for this club →
In McMichael, McTavish, Berggren, Barron, Carlo, Johnston, Proskurin, Pekarcik, Dube, Rosen, Peterson, Cranley
Callup Lindstein, Dean, Jiricek
Out Kyrou→WSH, Faulk→DET, Schenn→NYI, Drouin, Texier→MTL, Sundqvist, Joseph→EDM, Walker→UFA
16.7→18.8 +2.1
15.8→16.7 +0.9
23.4→24.2 +0.8
19→19.8 +0.8
18.3→19.1 +0.8
19.4→18.6 -0.8
15.9→15 -0.9
9.7→7.8 -1.9
Biggest projected minute changes either way — a club's ice time is a fixed budget, so the departures above are what free it up. Full board →
Top power-play unit — forward slotUNDERDEPLOYED6 pts at stake
holds it
53 proj pts · 17.4′ · 2.4′ PP
vs
pushing
61 proj pts · 19.1′ · 2.3′ PP
Pavel Buchnevichmodel favours the challengerDylan Holloway
1.40 more min/game on PP1 (measured PP1 vs PP2 minutes), at the challenger's own scoring rate over a full season. Power-play gaps come from measured PP minutes last season; even-strength gaps from the role model that drives every projection on this page.
Second power-play unit — forward slot7.8 pts at stake
holds it
25 proj pts · 14.8′ · 2′ PP
vs
pushing
35 proj pts · 15′ · 1.2′ PP
Dalibor Dvorskymodel favours the incumbentPius Suter
Conversion
17.6%
on the man advantage
PP goals
46
478 shots
Expected goals
50.9
-4.9 vs actual
Shooting
9.6%
of PP shots go in
What left the power play
Drouin carried 4% of the power-play points on 1% of its minutes — a focal score of 4.3. He is not on this roster.
Kyrou carried 10% of the power-play points on 8% of its minutes — a focal score of 1.23. He is not on this roster.
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
2.57′54%311145.12.866%1.19
2.44′51%411154.564.971%1.07
1.7′36%1893.880.778%0.9
1.34′28%3033.730.8—0.88
2.33′49%4483.53.658%0.82
2.35′50%5493.286.351%0.76
1.21′26%1343.091.4—0.72
2′42%5272.953.360%0.7
2.17′46%1672.812.248%0.66
1.44′30%2352.580.952%0.6
What IPP, focal and ixG mean
IPP is the share of the power-play goals he was on the ice for that he got a point on. It is the direct form of “the play runs through him”: two men can take the same unit minutes while one touches the puck on every goal and the other watches from the far circle. League average on the power play is 59%. It is computed from the on-ice record of every goal, corrected for the ~18% of goals with no on-ice row and shrunk toward the positional mean for thin samples, so it cannot exceed 1.
Focal is the older, cruder version — his share of the team's power-play points over his share of its minutes. It is team-relative, so it moves when a teammate is injured; IPP does not. ixG is the expected goals from his own shots on the man advantage. Minutes are measured, not modelled.
Focal is the older, cruder version — his share of the team's power-play points over his share of its minutes. It is team-relative, so it moves when a teammate is injured; IPP does not. ixG is the expected goals from his own shots on the man advantage. Minutes are measured, not modelled.
Projected PP1Buchnevich15 PPP (15 last yr)Thomas20 PPP (14 last yr)Snuggerud15 PPP (9 last yr)Broberg6 PPP (5 last yr)McTavish14 PPP (11 last yr)
Projected PP2Neighbours10 PPP (7 last yr)Dvorsky8 PPP (7 last yr)Fowler8 PPP (9 last yr)Holloway11 PPP (8 last yr)McMichael7 PPP (5 last yr)
Hits
150617th
projected, this roster · of 32
Blocks
114315th
projected, this roster · of 32
Shots
206430th
projected, this roster · of 32
Penalty minutes
61619th
projected, this roster · of 32
Faceoff wins
219116th
projected, this roster · of 32
H+B
264817th
projected, this roster · of 32
S+H+B
471226th
projected, this roster · of 32
What actually moves these numbers
Penalty-kill time is the strongest driver of blocks we can measure. Across 1,504 consecutive season pairs, a skater who gains a minute of shorthanded time a game adds a median 9.5 blocks over 84 games — a defenceman 16 — and the effect runs monotonically the other way too: lose a minute and it is −15.6, or −25.9 for a defenceman. The PK column is here for that reason.
But it is mostly the minutes, not the man. Shorthanded time correlates +0.52 with blocks per 60 across players and only +0.14 within the same player year to year, which says coaches pick shot-blockers for the kill more than the kill turns anyone into one. A player promoted onto PK1 gains blocks because he is on the ice for more shots, not because he changed — so price the ice time, not a breakout.
Hits run opposite to ice time. Total minutes correlate −0.57 with hits per 60, and −0.60 among forwards: the fourth line hits, the first line does not. So a rate beats a total here more than in any other category, which is what the /60 columns are for — a checker on eleven minutes at 12 hits per 60 is worth more than a first-liner at 5 on nineteen, and their season totals can look identical.
Power-play time predicts the absence of all of it — −0.43 with hits per 60, −0.47 with blocks, −0.26 with penalty minutes. The men who run a power play are not the men who supply a banger roster, which is why the two sections of this page rarely name anybody twice.
Faceoff wins are a volume category. They track draws taken far more than win rate, so the man to want is the one taking the most — normally the first-line centre and whoever takes defensive-zone draws on the kill.
Measured on 2022-23 through 2025-26, skaters with 40+ games and 200+ minutes at both ends of each pair.
But it is mostly the minutes, not the man. Shorthanded time correlates +0.52 with blocks per 60 across players and only +0.14 within the same player year to year, which says coaches pick shot-blockers for the kill more than the kill turns anyone into one. A player promoted onto PK1 gains blocks because he is on the ice for more shots, not because he changed — so price the ice time, not a breakout.
Hits run opposite to ice time. Total minutes correlate −0.57 with hits per 60, and −0.60 among forwards: the fourth line hits, the first line does not. So a rate beats a total here more than in any other category, which is what the /60 columns are for — a checker on eleven minutes at 12 hits per 60 is worth more than a first-liner at 5 on nineteen, and their season totals can look identical.
Power-play time predicts the absence of all of it — −0.43 with hits per 60, −0.47 with blocks, −0.26 with penalty minutes. The men who run a power play are not the men who supply a banger roster, which is why the two sections of this page rarely name anybody twice.
Faceoff wins are a volume category. They track draws taken far more than win rate, so the man to want is the one taking the most — normally the first-line centre and whoever takes defensive-zone draws on the kill.
Measured on 2022-23 through 2025-26, skaters with 40+ games and 200+ minutes at both ends of each pair.
$94.0Mcommitted · 24 of 24 on file
11reach the market after this season
Pending free agents · this summer
| Jordan BinningtonG | UFA | $6.00M | |
|---|---|---|---|
| Pius SuterC | UFA | $4.13M | 35 pts |
| Brandon CarloD | UFA | $4.10M | 9 pts |
| Jake NeighboursL | RFA | $3.75M | 41 pts |
| Joel HoferG | RFA | $3.40M | |
| Jonatan BerggrenR | RFA | $2.00M | 21 pts |
| Justin BarronD | RFA | $1.57M | 10 pts |
| Jimmy SnuggerudR | RFA | $0.95M | 68 pts |
| Tyler TuckerD | UFA | $0.93M | 4 pts |
| Logan MaillouxD | RFA | $0.85M | 4 pts |
| Zach DeanC | RFA | $0.85M | 1 pts |
Free the summer after
| Alexey ToropchenkoR | $2.50M | 5 pts |
|---|---|---|
| Theo LindsteinD | $0.94M | 15 pts |
| Dalibor DvorskyC | $0.91M | 25 pts |
Biggest cap hits
| Robert ThomasC | $8.13M | 4y left · NTC |
|---|---|---|
| Pavel BuchnevichL | $8.00M | 4y left · NTC |
| Philip BrobergD | $8.00M | 5y left |
| Dylan HollowayL | $7.75M | 4y left |
| Mason McTavishC | $7.00M | 4y left |
| Connor McMichaelC | $6.75M | 5y left |
| Colton ParaykoD | $6.50M | 3y left · NTC |
| Cam FowlerD | $6.10M | 2y left · NTC |
Cap hits from CapWages for the 24 men on file. A dash is a figure we do not have, usually a restricted free agent whose entry-level deal has lapsed, and those are left out of the committed total rather than counted as nothing.
GSAx view
Hofer
43 NHL starts last season
GSAx / start
0.36lg -0.040587th pctile
Shot quality faced
0.1036lg 0.10446th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
48 GS26 W (15–34)0.899 SV%2.98 GAA
2025-26 actual · NHL
43 GS24 W0.910 SV%2.61 GAA
Binnington
39 NHL starts last season
GSAx / start
-0.745lg -0.04051st pctile
Shot quality faced
0.098lg 0.1046th hardest
10-start rolling GSAx · appearance 1-81 · shared scale
2026-27 projection
34 GS15 W (9–20)0.882 SV%3.29 GAA
2025-26 actual · NHL
39 GS13 W0.873 SV%3.33 GAA
GSAx is goals saved above expected — what the shots he faced were worth, minus what he actually allowed. Shown per start, because a backup cannot out-accumulate a starter but can out-perform him on a per-night basis. Shot quality faced is expected goals per shot: higher means he was hung out to dry more often, and the percentile is where that workload ranks among goalies with 15+ starts. Rolling form is a 10-start moving average — is he playing well lately; running total is the season's accumulated damage. Per-game GSAx is too noisy to read either from directly.
Projections — Every Skater
the reference table · Overall is value as a z-score against the league, so +1.00 is a standard deviation above average‹ 12 / 12Forwards · 14
| Player | Role | Age | Overall | ADP | GP | G | A | P / if fit | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dylan Holloway | L1·PP2 | 25 | +0.97 | 101 | 72 | 24 | 36 | 60.8/68 | 11 | 0 | 184 | 156 | 46 | 23 | +10 | 44 | 202 | 386 | ascending | |
| Jake Neighbours | L3·PP2 | 24 | +0.87 | 249.4 | 78 | 18 | 24 | 41.4 | 10 | 0 | 110 | 166 | 44 | 47 | -5 | 6 | 210 | 320 | ||
| Ross Johnston | L4 | 32 | +0.64 | 291.8 | 62 | 2 | 4 | 5.5 | 0 | 0 | 29 | 180 | 20 | 99 | -1 | 4 | 200 | 229 | ice time ↓ | |
| Mason McTavish | L2·PP1 | 23 | +0.58 | 205.5 | 78 | 23 | 29 | 52.5 | 14 | 0 | 167 | 90 | 35 | 46 | -9 | 424 | 124 | 291 | bounce-backPP1 | |
| Jimmy Snuggerud | L1·PP1 | 22 | +0.51 | 163.7 | 76 | 28 | 40 | 68.2 | 15 | 0 | 206 | 77 | 42 | 23 | +7 | 4 | 118 | 324 | — | sell-highice time ↑PP1 |
| Dillon Dube | 28 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | declining | ||
| Connor McMichael | L2·PP2 | 25 | -0.07 | 231.5 | 81 | 21 | 32 | 53.4 | 7 | 1 | 151 | 45 | 43 | 36 | +6 | 226 | 88 | 240 | ascendingbounce-back | |
| Robert Thomas | L1·PP1 | 27 | -0.13 | 114.4 | 76 | 24 | 56 | 79.1 | 20 | 3 | 134 | 14 | 47 | 28 | +18 | 706 | 61 | 195 | ascendingPP1 | |
| Pavel Buchnevich | L2·PP1 | 31 | -0.34 | 215.2 | 80 | 22 | 32 | 53.3 | 15 | 1 | 139 | 36 | 37 | 28 | -3 | 93 | 72 | 212 | decliningPP1 | |
| Dalibor Dvorsky | L3·PP2 | 21 | -0.60 | 292.9 | 73 | 14 | 12 | 25.2 | 8 | 0 | 114 | 80 | 28 | 29 | -2 | 345 | 108 | 222 | — | |
| Pius Suter | L4 | 30 | -0.86 | 292.5 | 77 | 17 | 18 | 34.9 | 4 | 2 | 112 | 51 | 32 | 21 | +9 | 318 | 84 | 196 | ||
| Alexey Toropchenko | 27 | -1.14 | 292.3 | 49 | 2 | 3 | 4.8 | 0 | 0 | 53 | 109 | 37 | 19 | -4 | 3 | 146 | 199 | |||
| Jonatan Berggren | L3 | 26 | -1.65 | 292.6 | 63 | 8 | 13 | 21.4/28 | 3 | 0 | 73 | 44 | 18 | 15 | -5 | 4 | 62 | 135 | ||
| Zach Dean | L4 | 23 | -3.05 | — | 4 | 0 | 1 | 1/15 | 0 | 0 | 4 | 7 | 2 | 2 | 0 | 15 | 9 | 13 | — |
Shading is that man's percentile among all projected forwards in the league, not among these 14. top 10% top 20 top 30 bottom 20. H+B and S+H+B take the weaker leg, because good at both is a floor rather than an average. P, SHP, PIM, +/- and FOW are unshaded — the percentile block does not carry them.
Defence · 9
| Player | Role | Age | Overall | ADP | GP | G | A | P / if fit | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Colton Parayko | RD2 | 33 | +0.82 | 200.6 | 76 | 6 | 14 | 19.6 | 0 | 1 | 109 | 96 | 167 | 20 | -1 | 0 | 263 | 371 | decliningbounce-back | |
| Brandon Carlo | RD3 | 30 | +0.59 | 292.5 | 77 | 2 | 7 | 8.5 | 0 | 1 | 80 | 94 | 137 | 44 | +8 | 0 | 231 | 311 | ||
| Philip Broberg | LD1·PP1 | 25 | -0.48 | 177.5 | 76 | 7 | 26 | 32.5 | 6 | 0 | 95 | 37 | 97 | 18 | +10 | 0 | 134 | 229 | ascendingPP1 | |
| Theo Lindstein | LD2 | 21 | -0.69 | 292.7 | 84 | 7 | 9 | 15.1 | 2 | 0 | 85 | 53 | 86 | 19 | 0 | 0 | 139 | 224 | — | |
| Justin Barron | RD1 | 25 | -0.74 | — | 69 | 3 | 8 | 10.1 | 0 | 0 | 59 | 64 | 85 | 25 | -4 | 0 | 149 | 208 | ||
| Cam Fowler | LD3·PP2 | 35 | -1.11 | 237.5 | 76 | 5 | 25 | 29.3 | 8 | 1 | 82 | 11 | 77 | 15 | -7 | 0 | 88 | 170 | ||
| Tyler Tucker | 26 | -1.59 | 292.5 | 31 | 1 | 3 | 3.9/10 | 0 | 0 | 29 | 50 | 27 | 36 | -1 | 0 | 77 | 106 | |||
| Logan Mailloux | 23 | -1.88 | 290.7 | 30 | 1 | 2 | 3.8/10 | 1 | 0 | 39 | 41 | 29 | 20 | -2 | 0 | 69 | 109 | — | bounce-back | |
| Adam Jiricek | 20 | -2.89 | 292.4 | 10 | 1 | 2 | 3.2/23 | 0 | 0 | 10 | 7 | 9 | 3 | 0 | 0 | 16 | 26 | — |
Shading is that man's percentile among all projected defencemen in the league, not among these 9. top 10% top 20 top 30 bottom 20. H+B and S+H+B take the weaker leg, because good at both is a floor rather than an average. P, SHP, PIM, +/- and FOW are unshaded — the percentile block does not carry them.
Goalies · 3
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Joel Hofer | 48 | 26 | 16 | 5 | 0.899 | 2.98 | 1248 | 1387 | 140 | 5.4 | +15.5 | 0.36 |
| Jordan Binnington | 34 | 15 | 16 | 4 | 0.882 | 3.29 | 815 | 924 | 109 | 1.3 | -29.1 | -0.745 |
| Georgii Romanov | — | — | — | — | — | — | — | — | — | — | 0 | — |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.